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Long-term steel reinforcement corrosion greatly impacts reinforced concrete structures, particularly in marine and coastal settings. Concrete failure leads to human casualties, requiring extensive demolition and maintenance, which represents an inefficient use of energy and resources. This study utilizes microscopic observation, atomic force microscopy (SKPM), electrochemical experiments, and XPS analysis to investigate the corrosion behavior of 500CE and 500E under identical conditions. We compared 500E with 500CE, supplemented with 0.94% Cr, 0.46% Mo, 0.37% Ni, and 0.51% Cu through alloying element regulation to obtain a finer ferrite grain and lower pearlitic content. The results indicate that 500CE maintains a stable potential, whereas 500E exhibits larger grain sizes and significant surface potential fluctuations, which may predispose it to corrosion. In addition, despite its more uniform microstructure and stable electrochemical activity, 500E shows inferior corrosion resistance under prolonged exposure. The electrochemical corrosion rate of 500CE in both the pristine and passivated states and for various passivation durations is slower than that of 500E, indicating superior corrosion performance. Notably, there is a significant increase in the corrosion rate of 500E after 144 h of exposure. This study provides valuable insights into the chloride corrosion phenomena of low-alloy corrosion-resistant steel reinforcement in service, potentially enhancing the longevity of reinforced concrete structures. Full article

(This article belongs to the Special Issue Inclusion Metallurgy (2nd Edition))

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24 pages, 3637 KiB

Open AccessPerspective

Real-Time Analysis of Neuronal Cell Cultures for CNS Drug Discovery

by Millicent T. Akere, Kelsee K. Zajac, James D. Bretz, Anvitha R. Madhavaram, Austin C. Horton and Isaac T. Schiefer

Brain Sci. 2024, 14(8), 770; https://doi.org/10.3390/brainsci14080770 (registering DOI) - 30 Jul 2024

Abstract

The ability to screen for agents that can promote the development and/or maintenance of neuronal networks creates opportunities for the discovery of novel agents for the treatment of central nervous system (CNS) disorders. Over the past 10 years, advances in robotics, artificial intelligence, and machine learning have paved the way for the improved implementation of live-cell imaging systems for drug discovery. These instruments have revolutionized our ability to quickly and accurately acquire large standardized datasets when studying complex cellular phenomena in real-time. This is particularly useful in the field of neuroscience because real-time analysis can allow efficient monitoring of the development, maturation, and conservation of neuronal networks by measuring neurite length. Unfortunately, due to the relative infancy of this type of analysis, standard practices for data acquisition and processing are lacking, and there is no standardized format for reporting the vast quantities of data generated by live-cell imaging systems. This paper reviews the current state of live-cell imaging instruments, with a focus on the most commonly used equipment (IncuCyte systems). We provide an in-depth analysis of the experimental conditions reported in publications utilizing these systems, particularly with regard to studying neurite outgrowth. This analysis sheds light on trends and patterns that will enhance the use of live-cell imaging instruments in CNS drug discovery. Full article

(This article belongs to the Section Molecular and Cellular Neuroscience)

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11 pages, 900 KiB

Open AccessArticle

Conventional and Novel Inflammatory Biomarkers in Chronic Heart Failure Patients with Atrial Fibrillation

by Gregor Vercek, Borut Jug, Marko Novakovic, Miha Antonic, Anze Djordjevic and Jus Ksela

Medicina 2024, 60(8), 1238; https://doi.org/10.3390/medicina60081238 (registering DOI) - 30 Jul 2024

Abstract

(1) Background and Objectives: Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with increased morbidity and mortality both in the general population and heart failure patients. Inflammation may promote the initiation, maintenance and perpetuation of AF, but the impact of inflammatory molecular signaling on the association between AF and heart failure remains elusive. (2) Materials and Methods: In 111 patients with chronic stable heart failure, baseline values of conventional (IL-6 and hsCRP) and selected novel inflammatory biomarkers (IL-10, IL-6/IL-10 ratio, orosomucoid and endocan) were determined. Inflammatory biomarkers were compared with respect to the presenting cardiac rhythm. (3) Results: Patients aged below 75 years with AF had significantly higher values of IL-6 and IL-6/IL-10 ratio; IL-6 levels were a significant predictor of AF in both univariate (OR 1.175; 95%CI 1.013–1.363; p = 0.034) and multivariate logistic regression analysis when accounting for other inflammatory biomarkers (OR 1.327; 95% CI 1.068–1.650; p = 0.011). Conversely, there was no association between other novel inflammatory biomarkers and AF. (4) Conclusions: IL-6 levels and the IL-6/IL-10 ratio are associated with AF in patients with chronic stable heart failure under the age of 75 years, suggesting that inflammatory molecular signaling may play a role in the development of AF in the heart failure population. Full article

(This article belongs to the Section Cardiology)

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12 pages, 991 KiB

Open AccessReview

Mitochondrial NME6: A Paradigm Change within the NME/NDP Kinase Protein Family?

by Bastien Proust, Maja Herak Bosnar, Helena Ćetković, Malgorzata Tokarska-Schlattner and Uwe Schlattner

Cells 2024, 13(15), 1278; https://doi.org/10.3390/cells13151278 (registering DOI) - 30 Jul 2024

Abstract

Eukaryotic NMEs/NDP kinases are a family of 10 multifunctional proteins that occur in different cellular compartments and interact with various cellular components (proteins, membranes, and DNA). In contrast to the well-studied Group I NMEs (NME1–4), little is known about the more divergent Group II NMEs (NME5–9). Three recent publications now shed new light on NME6. First, NME6 is a third mitochondrial NME, largely localized in the matrix space, associated with the mitochondrial inner membrane. Second, while its monomeric form is inactive, NME6 gains NDP kinase activity through interaction with mitochondrial RCC1L. This challenges the current notion that mammalian NMEs require the formation of hexamers to become active. The formation of complexes between NME6 and RCC1L, likely heterodimers, seemingly obviates the necessity for hexamer formation, stabilizing a NDP kinase-competent conformation. Third, NME6 is involved in mitochondrial gene maintenance and expression by providing (d)NTPs for replication and transcription (in particular the pyrimidine nucleotides) and by a less characterized mechanism that supports mitoribosome function. This review offers an overview of NME evolution and structure and highlights the new insight into NME6. The new findings position NME6 as the most comprehensively studied protein in NME Group II and may even suggest it as a new paradigm for related family members. Full article

(This article belongs to the Section Mitochondria)

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17 pages, 3342 KiB

Open AccessArticle

Sustainable Pavement Management: Harnessing Advanced Machine Learning for Enhanced Road Maintenance

by Kshitij Ijari and Carlos D. Paternina-Arboleda

Appl. Sci. 2024, 14(15), 6640; https://doi.org/10.3390/app14156640 (registering DOI) - 30 Jul 2024

Abstract

In this study, we introduce an advanced system for sustainable pavement management that leverages cutting-edge machine learning and computer vision techniques to detect and classify pavement damage. By utilizing models such as EfficientNetB3, ResNet18, and ResNet50, we develop robust classifiers capable of accurately identifying various types of pavement distress. To further enhance our dataset, we employ a Swin Transformer-based Generative Adversarial Network (GAN) to synthetically generate images of pavement cracks, thereby augmenting the training data. Our approach aims to improve the efficiency and accuracy of pavement damage assessment, contributing to more effective and sustainable road maintenance practices. This research aligns with the sustainable development goals by fostering innovative methods that extend the lifespan of infrastructure, reducing the need for resource-intensive repairs, and promoting the longevity and reliability of road networks. The outcomes of this study are discussed in terms of their potential impact on infrastructure safety and sustainability, with suggestions for future research directions. This study demonstrates how integrating advanced machine learning techniques into pavement management systems can enhance decision-making, optimize resource allocation, and improve the sustainability of infrastructure maintenance practices. By leveraging big data and sophisticated algorithms, stakeholders can proactively address pavement deterioration, extend asset lifespan, and optimize maintenance efforts based on real-time data-driven insights. Full article

(This article belongs to the Special Issue Advanced Pavement Engineering: Design, Construction, and Performance)

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25 pages, 2807 KiB

Open AccessReview

XAI-Based Clinical Decision Support Systems: A Systematic Review

by Se Young Kim, Dae Ho Kim, Min Ji Kim, Hyo Jin Ko and Ok Ran Jeong

Appl. Sci. 2024, 14(15), 6638; https://doi.org/10.3390/app14156638 (registering DOI) - 30 Jul 2024

Abstract

With increasing electronic medical data and the development of artificial intelligence, clinical decision support systems (CDSSs) assist clinicians in diagnosis and prescription. Traditional knowledge-based CDSSs follow an accumulated medical knowledgebase and a predefined rule system, which clarifies the decision-making process; however, maintenance cost issues exist in the medical data quality control and standardization processes. Non-knowledge-based CDSSs utilize vast amounts of data and algorithms to effectively make decisions; however, the deep learning black-box problem causes unreliable results. EXplainable Artificial Intelligence (XAI)-based CDSSs provide valid rationales and explainable results. These systems ensure trustworthiness and transparency by showing the recommendation and prediction result process using explainable techniques. However, existing systems have limitations, such as the scope of data utilization and the lack of explanatory power of AI models. This study proposes a new XAI-based CDSS framework to address these issues; introduces resources, datasets, and models that can be utilized; and provides a foundation model to support decision-making in various disease domains. Finally, we propose future directions for CDSS technology and highlight societal issues that need to be addressed to emphasize the potential of CDSSs in the future. Full article

(This article belongs to the Special Issue Artificial Intelligence in Medicine and Healthcare)

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21 pages, 3337 KiB

Open AccessReview

Neurotrophins and Their Receptors: BDNF’s Role in GABAergic Neurodevelopment and Disease

by Carlos Hernández-del Caño, Natalia Varela-Andrés, Alejandro Cebrián-León and Rubén Deogracias

Int. J. Mol. Sci. 2024, 25(15), 8312; https://doi.org/10.3390/ijms25158312 (registering DOI) - 30 Jul 2024

Abstract

Neurotrophins and their receptors are distinctly expressed during brain development and play crucial roles in the formation, survival, and function of neurons in the nervous system. Among these molecules, brain-derived neurotrophic factor (BDNF) has garnered significant attention due to its involvement in regulating GABAergic system development and function. In this review, we summarize and compare the expression patterns and roles of neurotrophins and their receptors in both the developing and adult brains of rodents, macaques, and humans. Then, we focus on the implications of BDNF in the development and function of GABAergic neurons from the cortex and the striatum, as both the presence of BDNF single nucleotide polymorphisms and disruptions in BDNF levels alter the excitatory/inhibitory balance in the brain. This imbalance has different implications in the pathogenesis of neurodevelopmental diseases like autism spectrum disorder (ASD), Rett syndrome (RTT), and schizophrenia (SCZ). Altogether, evidence shows that neurotrophins, especially BDNF, are essential for the development, maintenance, and function of the brain, and disruptions in their expression or signaling are common mechanisms in the pathophysiology of brain diseases. Full article

(This article belongs to the Special Issue Disorders in Brain Development and Nervous System: Key Molecules and Pathology)

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19 pages, 6382 KiB

Open AccessArticle

Tool Condition Monitoring in the Milling Process Using Deep Learning and Reinforcement Learning

by Devarajan Kaliyannan, Mohanraj Thangamuthu, Pavan Pradeep, Sakthivel Gnansekaran, Jegadeeshwaran Rakkiyannan and Alokesh Pramanik

J. Sens. Actuator Netw. 2024, 13(4), 42; https://doi.org/10.3390/jsan13040042 (registering DOI) - 30 Jul 2024

Abstract

Tool condition monitoring (TCM) is crucial in the machining process to confirm product quality as well as process efficiency and minimize downtime. Traditional methods for TCM, while effective to a degree, often fall short in real-time adaptability and predictive accuracy. This research work aims to advance the state-of-the-art methods in predictive maintenance for TCM and improve tool performance and reliability during the milling process. The present work investigates the application of Deep Learning (DL) and Reinforcement Learning (RL) techniques to monitor tool conditions in milling operations. DL models, including Long Short-Term Memory (LSTM) networks, Feed Forward Neural Networks (FFNN), and RL models, including Q-learning and SARSA, are employed to classify tool conditions from the vibration sensor. The performance of the selected DL and RL algorithms is evaluated through performance metrics like confusion matrix, recall, precision, F1 score, and Receiver Operating Characteristics (ROC) curves. The results revealed that RL based on SARSA outperformed other algorithms. The overall classification accuracies for LSTM, FFNN, Q-learning, and SARSA were 94.85%, 98.16%, 98.50%, and 98.66%, respectively. In regard to predicting tool conditions accurately and thereby enhancing overall process efficiency, SARSA showed the best performance, followed by Q-learning, FFNN, and LSTM. This work contributes to the advancement of TCM systems, highlighting the potential of DL and RL techniques to revolutionize manufacturing processes in the era of Industry 5.0. Full article

(This article belongs to the Special Issue Fault Diagnosis in the Internet of Things Applications)

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27 pages, 1885 KiB

Open AccessReview

Response to Replication Stress and Maintenance of Genome Stability by WRN, the Werner Syndrome Protein

by David K. Orren and Amrita Machwe

Int. J. Mol. Sci. 2024, 25(15), 8300; https://doi.org/10.3390/ijms25158300 (registering DOI) - 30 Jul 2024

Viewed by 87

Abstract

Werner syndrome (WS) is an autosomal recessive disease caused by loss of function of WRN. WS is a segmental progeroid disease and shows early onset or increased frequency of many characteristics of normal aging. WRN possesses helicase, annealing, strand exchange, and exonuclease activities and acts on a variety of DNA substrates, even complex replication and recombination intermediates. Here, we review the genetics, biochemistry, and probably physiological functions of the WRN protein. Although its precise role is unclear, evidence suggests WRN plays a role in pathways that respond to replication stress and maintain genome stability particularly in telomeric regions. Full article

(This article belongs to the Special Issue Mechanisms of DNA Replication Fork Progression, Stalling, and Rescue 2.0)

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30 pages, 3680 KiB

Open AccessArticle

Language Attitudes in a Historic Latino Community: The Case of Spanish in Northwest Indiana

by Eva Mendieta

Languages 2024, 9(8), 263; https://doi.org/10.3390/languages9080263 (registering DOI) - 30 Jul 2024

Viewed by 197

Abstract

Despite the burgeoning Latino population in the Midwest, research on language attitudes in this region remains sparse. This study addresses this gap by examining language attitudes and beliefs towards Spanish in the Northwest Indiana region, one of the oldest Latino immigration gateways in the Midwest. Data collected from a 2018–2019 sociolinguistic survey, involving 236 participants representative of the local Latino community, form the basis of the analysis. The study aims to elucidate attitudes towards various Spanish dialects, particularly the local variety. Findings indicate widespread acceptance of the local Spanish variety, with participants viewing its divergence from Mexican or Puerto Rican Spanish as normal and inevitable. Despite perceptions of linguistic mixing with English, the community’s Spanish is valued as an effective communication tool and cultural asset, including in educational settings. This positive attitude towards a stigmatized linguistic variety suggests a preference for any form of Spanish over none, particularly in situations of low Spanish language maintenance. The study of language attitudes shows that speakers will tend to reproduce in their speech new ways of speaking that they find acceptable. This generalized behavior, in turn, leads toward linguistic change. Full article

(This article belongs to the Special Issue Spanish in the US: A Sociolinguistic Approach)

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22 pages, 2652 KiB

Open AccessArticle

Exploring Musculoskeletal Complaints in a Needle Manufacturing Industry: A Cross-Sectional Study

by Paulo C. Anacleto Filho, Ana Cristina Braga and Paula Carneiro

Int. J. Environ. Res. Public Health 2024, 21(8), 996; https://doi.org/10.3390/ijerph21080996 (registering DOI) - 29 Jul 2024

Viewed by 244

Abstract

Musculoskeletal disorders (MSD) encompass a variety of conditions affecting muscles, joints, and nerves. In Portugal, MSDs are the most prevalent occupational health problem in companies. Based on the relevance of work-related MSD (WMSD), this study aims to assess the prevalence of MSD complaints in a needle manufacturing industry in Northern Portugal, following a cross-sectional approach. Thus, 526 workers from five departments (i.e., operator, tuning, maintenance, administration, and logistics) answered a sociodemographic questionnaire and the Nordic Musculoskeletal Questionnaire (NMQ). Within the last 12 months, females exhibited a higher frequency of complaints than males across all body parts except for ankles/feet. The body parts eliciting the most percentage of complaints for both genders include the lower back (54.2%), neck (42.2%), shoulders (39.0%), ankles/feet (38.2%), and wrists/hands (35.7%). No significant association was found between Body Mass Index (BMI) and body part complaints. Tuners reported the highest complaint rate, with occupations as substantial predictors of complaints in certain body parts. Likewise, complaints tend to increase with age. The findings advocate for ergonomic interventions that are gender-, age-, and job-sensitive. Full article

(This article belongs to the Special Issue Advances in Ergonomics, Human Factors and Safety for Occupational Health)

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12 pages, 919 KiB

Open AccessArticle

Rising Prevalence of Low-Frequency PPM1D Gene Mutations after Second HDCT in Multiple Myeloma

by Katja Seipel, Nuria Z. Veglio, Henning Nilius, Barbara Jeker, Ulrike Bacher and Thomas Pabst

Curr. Issues Mol. Biol. 2024, 46(8), 8197-8208; https://doi.org/10.3390/cimb46080484 (registering DOI) - 29 Jul 2024

Viewed by 115

Abstract

Multiple myeloma (MM) first-line treatment algorithms include immuno-chemotherapy (ICT) induction, high-dose chemotherapy (HDCT) and autologous stem cell transplant (ASCT) consolidation, followed by lenalidomide maintenance. After these initial therapies, most patients suffer a disease relapse and require subsequent treatment lines including ICT, additional HDCT and ASCT, or novel immunotherapies. The presence of somatic mutations in peripheral blood cells has been associated with adverse outcomes in a variety of hematological malignancies. Nonsense and frameshift mutations in the PPM1D gene, a frequent driver alteration in clonal hematopoiesis (CH), lead to the gain-of-function of Wip1 phosphatase, which may impair the p53-dependent G1 checkpoint and promote cell proliferation. Here, we determined the presence of PPM1D gene mutations in peripheral blood cells of 75 subsequent myeloma patients in remission after first or second HDCT/ASCT. The prevalence of truncating PPM1D gene mutations emerged at 1.3% after first HDCT/ASCT, and 7.3% after second HDCT/ASCT, with variant allele frequencies (VAF) of 0.01 to 0.05. Clinical outcomes were inferior in the PPM1D-mutated (PPM1Dmut) subset with median progression-free survival (PFS) of 15 vs. 37 months (p = 0.0002) and median overall survival (OS) of 36 vs. 156 months (p = 0.001) for the PPM1Dmut and PPM1Dwt population, respectively. Our data suggest that the occurrence of PPM1D gene mutations in peripheral blood cells correlates with inferior outcomes after ASCT in patients with multiple myeloma. Full article

(This article belongs to the Special Issue Multiple Myeloma: From Molecular Mechanism to Diagnosis and Therapy)

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22 pages, 940 KiB

Open AccessArticle

Reliability, Availability, and Maintainability Assessment-Based Sustainability-Informed Maintenance Optimization in Power Transmission Networks

by Motahareh Sagharidooz, Hamzeh Soltanali, José Torres Farinha, Hugo D. N. Raposo and José Edmundo de-Almeida-e-Pais

Sustainability 2024, 16(15), 6489; https://doi.org/10.3390/su16156489 - 29 Jul 2024

Viewed by 347

Abstract

Reliable and resilient power transmission networks serve as vital for sustainable development and uninterrupted electricity supply. Effective maintenance programs are necessary to comply with reliability and sustainability requirements in the power sector. To that end, RAM (reliability, availability, and maintainability) assessments can provide efficient maintenance services that minimize adverse consequences and increase productivity at the lowest possible cost. We employ a statistical framework to evaluate RAM principles, including data acquisition, homogenization, trend hypothesis validation, and parameter estimation. The RAM evaluation of power transmission networks identifies primary bottlenecks in subsystems based on failure and repair behavior trends, which should be prioritized. To find the optimal maintenance policies for each subsystem, we adapt a Multi-Attribute Utility Theory (MAUT) approach, taking costs, availability, and dependability into account. The results of this approach can help improve the operational performance and sustainability of power transmission networks. Full article

(This article belongs to the Section Sustainable Management)

20 pages, 3892 KiB

Open AccessArticle

Data-Driven Golden Jackal Optimization–Long Short-Term Memory Short-Term Energy-Consumption Prediction and Optimization System

by Yongjie Yang, Yulong Li, Yan Cai, Hui Tang and Peng Xu

Energies 2024, 17(15), 3738; https://doi.org/10.3390/en17153738 - 29 Jul 2024

Viewed by 216

Abstract

In order to address the issues of significant energy and resource waste, low-energy management efficiency, and high building-maintenance costs in hot-summer and cold-winter regions of China, a research project was conducted on an office building located in Nantong. In this study, a data-driven golden jackal optimization (GJO)-based Long Short-Term Memory (LSTM) short-term energy-consumption prediction and optimization system is proposed. The system creates an equivalent model of the office building and employs the genetic algorithm tool Wallacei to automatically optimize and control the building’s air conditioning system, thereby achieving the objective of reducing energy consumption. To validate the authenticity of the optimization scheme, unoptimized building energy consumption was predicted using a data-driven short-term energy consumption-prediction model. The actual comparison data confirmed that the reduction in energy consumption resulted from implementing the air conditioning-optimization scheme rather than external factors. The optimized building can achieve an hourly energy saving rate of 6% to 9%, with an average daily energy-saving rate reaching 8%. The entire system, therefore, enables decision-makers to swiftly assess and validate the efficacy of energy consumption-optimization programs, thereby furnishing a scientific foundation for energy management and optimization in real-world buildings. Full article

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14 pages, 2257 KiB

Open AccessReview

Methylation Modification in Ornamental Plants: Impact on Floral Aroma and Color

by Chenchen Xie, Qingyin Tian, Hanruo Qiu, Rui Wang, Lianggui Wang, Yuanzheng Yue and Xiulian Yang

Int. J. Mol. Sci. 2024, 25(15), 8267; https://doi.org/10.3390/ijms25158267 (registering DOI) - 29 Jul 2024

Viewed by 156

Abstract

Methylation represents a crucial class of modification that orchestrates a spectrum of regulatory roles in plants, impacting ornamental characteristics, growth, development, and responses to abiotic stress. The establishment and maintenance of methylation involve the coordinated actions of multiple regulatory factors. Methyltransferases play a pivotal role by specifically recognizing and methylating targeted sites, which induces alterations in chromatin structure and gene expression, subsequently influencing the release of volatile aromatic substances and the accumulation of pigments in plant petals. In this paper, we review the regulatory mechanisms of methylation modification reactions and their effects on the changes in aromatic substances and pigments in plant petals. We also explore the potential of methylation modifications to unravel the regulatory mechanisms underlying aroma and color in plant petals. This aims to further elucidate the synthesis, metabolism, and regulatory mechanisms of various methylation modifications related to the aroma and color substances in plant petals, thereby providing a theoretical reference for improving the aroma and color of plant petals. Full article

(This article belongs to the Special Issue The Biochemistry, Molecular and Cell Biology beyond Flowers)

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