bluuebunny/crossref_metadata_2025 huggingface dataset:
Machine Learning to Predict Pregnancy in Dairy Cows: An Approach Integrating Automated Activity Monitoring and On-Farm Data
Authors: Marques Thaisa Campos , Marques Letícia Ribeiro , Fernandes Patrick Bezerra , de Lima Fabio Soares , do Prado Paim Tiago , Leão Karen Martins
Published: May 2024

Automated activity monitoring (AAM) systems are critical in the dairy industry for detecting estrus and optimizing the timing of artificial insemination (AI), thus enhancing pregnancy success rates in cows. This study developed a predictive model to improve pregnancy success by integrating AAM data with cow-specific and environmental factors. Utilizing data from 1,054 cows, this study compared the pregnancy outcomes between two AI timings—8 or 10 h post-AAM alarm. Variables such as age, parity, body condition, locomotion, and vaginal discharge scores, peripartum diseases, the breeding program, the bull used for AI, milk production at the time of AI, and environmental conditions (season, relative humidity, and temperature–humidity index) were considered alongside the AAM data on rumination, activity, and estrus intensity. Six predictive models were assessed to determine their efficacy in predicting pregnancy success: logistic regression, Bagged AdaBoost algorithm, linear discriminant, random forest, support vector machine, and Bagged Classification Tree. Integrating the on-farm data with AAM significantly enhanced the pregnancy prediction accuracy at AI compared to using AAM data alone. The random forest models showed a superior performance, with the highest Kappa statistic and lowest false positive rates. The linear discriminant and logistic regression models demonstrated the best accuracy, minimal false negatives, and the highest area under the curve. These findings suggest that combining on-farm and AAM data can significantly improve reproductive management in the dairy industry.
Effects of Low-Lipid Diets on Growth, Haematology, Histology and Immune Responses of Parr-Stage Atlantic Salmon (Salmo salar)
Authors: Lee Byoungyoon , Lee Junoh , Lim Saeyeon , Seong Minjae , Yun Hanbin , Han Sijun , Kim Kang-Woong , Lee Seunghan , Jeong Seong-Mok , Park Mun Chang , Hong Woo Seok , Kwon Se Ryun , Park Youngjin
Published: May 2024

Lipids in fish diets provide energy and play important roles in immunity and metabolism. Atlantic salmon, a species that migrates from freshwater to seawater, requires high energy, especially during smoltification. Juvenile teleosts have low lipid requirements, and a high dietary lipid content is known to have negative effects on their growth and digestion. Therefore, this study evaluated the effect of two commercial rainbow trout feeds (low-lipid, 13.41% and 14.6%) on the growth and immune responses of early parr-stage Atlantic salmon compared to commercial salmon feed (high-lipid, 29.52%). Atlantic salmon parr (weight: 14.56 ± 2.1 g; length: 11.23 ± 0.44 cm) were randomly divided into three groups and fed either one of two commercial rainbow trout feeds (RTF1 and RTF2) or the commercial salmon feed (ASF) for 12 weeks. At the end of the feeding trial, growth, haematology, histology and gene expression analyses were performed. There were no significant differences in weight gain rates or feed efficiency between the groups (p > 0.05). Superoxidate dismutase, glutathione peroxidase, lysozyme and immunoglobulin M activities were not different among the experimental groups (p > 0.05). A histological examination of the liver and intestinal tissues showed no pathological symptoms of inflammatory response or lipid accumulation in any of the groups. In an intestinal transcriptome analysis using RNA-seq, the expression levels of several genes linked to lipids, immune-related proteins, cytokines and chemokines did not differ significantly between the groups (p > 0.05). Commercial rainbow trout feed with low lipid content has no clear negative impact on the development of Atlantic salmon during the early parr stage (14.5 to 39.6 g). This study provides basic information for the development of economical feed for early parr-stage Atlantic salmon.
Differentiation of Synanthropic Fleas from Andalusia (Spain) through Geometric Morphometrics Analysis
Authors: García-Sánchez Angela M. , Trujillo Ignacio , Zurita Antonio , Cutillas Cristina
Published: May 2024

Fleas (Siphonaptera) are ectoparasitic hematophagous insects responsible for causing bites and itchy skin conditions in both humans and animals. Furthermore, they can act as vectors of different pathogens of a wide variety of diseases worldwide, including bartonellosis, rickettsiosis, and bubonic plague. Accurate identification of fleas is necessary for the study of their epidemiology, prevention, and control. In addition to traditional morphological classification approaches and molecular biology techniques, geometric morphometrics is increasingly proving to be a useful complementary tool for discriminating between Siphonaptera taxa. With the objective of determining the capacity of this technique to identify and differentiate synanthropic fleas, a principal component analysis was carried out on populations of Ctenocephalides felis, Pulex irritans, and Archaeopsylla erinacei collected in distinct regions of Andalusia (Spain). The analysis carried out on 81 male and female specimens revealed factorial maps that allowed the differentiation of the populations under study, with only partial overlaps that did not prevent their correct identification. Global size differences were also detected, with a slightly larger size in P. irritans males and a bigger size in A. erinacei females. Therefore, the present study emphasizes the role of geometric morphometrics as a useful complementary technique in taxonomic studies of arthropods, especially in the case of flea specimens lacking representative morphological features.
Increasing Risks to the Health of the Invertebrates—Balancing between Harm and Benefit
Authors: Kuznetsova Tatiana V. , Kudryavtseva Valentina A. , Kapranova Larisa L.
Published: May 2024

The article discusses the issue of extensive use of detergents and sanitizers in the time of new challenges associated with the COVID-19 (SARS-CoV-2) pandemic. These agents could pose threats to the existence of free-living invertebrates as essential components of the ecosystem. The biological effects of the mentioned classes of substances, their metabolites, and combined effects in the mixture have not been studied enough. The main challenges in trying to balance the threats and benefits of using such substances are the lack of knowledge of the biological effects of these products, the gaps in testing invertebrates’ responses, and changes in environment-related regulations to minimize risks to animals and humans. Numerous studies in this field still leave research gaps, particularly concerning the combined toxicity of well-known and widely used disinfectants, surfactants, and heavy metals, posing potential future challenges. Additionally, the review identified the need for additional testing of invertebrates for their sensitivity to disinfectants and surfactants of different compositions, including improved (non-invasive) methods, studies for early life stages, and comparative studies of species resilience.
Emergence of High-Level Gentamicin Resistance in Streptococcus agalactiae Hypervirulent Serotype IV ST1010 (CC452) Strains by Acquisition of a Novel Integrative and Conjugative Element
Authors: Creti Roberta , Imperi Monica , Khan Uzma Basit , Berardi Alberto , Recchia Simona , Alfarone Giovanna , Gherardi Giovanni
Published: May 2024

Streptococcus agalactiae (group B streptococci, GBS) is responsible for severe infections in both neonates and adults. Currently, empiric antimicrobial therapy for sepsis and meningitis is the combined use of penicillin and gentamicin due to the enhanced bactericidal activity. However, high-level gentamicin resistance (HLGR) abrogates the synergism. The rate of HLGR was investigated within a dataset of 433 GBS strains collected from cases of invasive disease in both adults and neonates as well as from pregnant carriers. GBS isolates (n = 20, 4.6%) presented with HLGR (gentamicin MIC breakpoint >1024 mg/L) that was differently diffused between strains from adults or neonates (5.2% vs. 2.8%). Notably, 70% of HLGR GBS strains (14 isolates) were serotype IV. Serotype IV HLGR-GBS isolates were susceptible to all antibiotics tested, exhibited the alpha-C/HvgA/PI-2b virulence string, and belonged to sequence type 1010 (clonal complex (CC) 452). The mobile element that harbored the HLGR aac(6′)-aph(2)″ gene is a novel integrative and conjugative element (ICE) about 45 kb long, derived from GBS 515 ICE tRNALys. The clonal expansion of this HLGR hypervirulent serotype IV GBS CC452 sublineage may pose a threat to the management of infections caused by this strain type.
Halicin: A New Horizon in Antibacterial Therapy against Veterinary Pathogens
Authors: Wang Shuge , Zhao Ke , Chen Ziqi , Liu Dejun , Tang Shusheng , Sun Chengtao , Chen Hongliang , Wang Yang , Wu Congming
Published: May 2024

It is crucial to discover novel antimicrobial drugs to combat resistance. This study investigated the antibacterial properties of halicin (SU3327), an AI-identified anti-diabetic drug, against 13 kinds of common clinical pathogens of animal origin, including multidrug-resistant strains. Employing minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) assessments, halicin demonstrated a broad-spectrum antibacterial effect. Time-killing assays revealed its concentration-dependent bactericidal activity against Escherichia coli ATCC 25922 (E. coli ATCC 25922), Staphylococcus aureus ATCC 29213 (S. aureus ATCC 29213), and Actinobacillus pleuropneumoniae S6 (APP S6) after 4 h of treatment at concentrations above the MIC. Halicin exhibited longer post-antibiotic effects (PAEs) and sub-MIC effects (PA-SMEs) for E. coli 25922, S. aureus 29213, and APP S6 compared to ceftiofur and ciprofloxacin, the commonly used veterinary antimicrobial agents, indicating sustained antibacterial action. Additionally, the results of consecutive passaging experiments over 40 d at sub-inhibitory concentrations showed that bacteria exhibited difficulty in developing resistance to halicin. Toxicology studies confirmed that halicin exhibited low acute toxicity, being non-mutagenic, non-reproductive-toxic, and non-genotoxic. Blood biochemical results suggested that halicin has no significant impact on hematological parameters, liver function, and kidney function. Furthermore, halicin effectively treated respiratory A. pleuropneumoniae infections in murine models. These results underscore the potential of halicin as a new antibacterial agent with applications against clinically relevant pathogens in veterinary medicine.
Exploring Chemical Composition, Antioxidant, Enzyme Inhibitory and Cytotoxic Properties of Glaucium acutidentatum Hausskn. & Bornm. from Turkey Flora: A Novel Source of Bioactive Agents to Design Functional Applications
Authors: Yagi Sakina , Zengin Gokhan , Uba Abdullahi Ibrahim , Maciejewska-Turska Magdalena , Sieniawska Elwira , Świątek Łukasz , Rajtar Barbara , Bahşi Muammer , Guler Osman , Dall’Acqua Stefano , Polz-Dacewicz Małgorzata
Published: May 2024

The present study was performed to determine the chemical constituents, cytotoxicity, antioxidant and enzyme inhibition activities of the aerial parts of Glaucium acutidentatum Hausskn. and Bornm. (family Papaveraceae). Methanolic and aqueous extracts were prepared by maceration, homogenizer-assisted extraction (HAE) and infusion. Results showed that the highest total phenolic and flavonoids contents were obtained from the methanol extracts obtained by HAE (53.22 ± 0.10 mg GAE/g) and maceration (30.28 ± 0.51 mg RE/g), respectively. The aporphine, beznyltetrahydroisoquinoline, and protopine types of Glaucium alkaloids have been tentatively identified. Among them, glaucine was identified in all extracts. Flavonoids, phenolic acids, coumarins, organic acids and fatty acids were also detected. Methanolic extract obtained using the HAE method displayed the highest anti-DPPH (41.42 ± 0.62 mg TE/g), total antioxidant (1.20 ± 0.17 mmol TE/g), Cu2+ (113.55 ± 6.44 mg TE/g), and Fe3+ (74.52 ± 4.74 mg TE/g) reducing properties. The aqueous extracts obtained by infusion and HAE methods exerted the best anti-ABTS (103.59 ± 1.49 mg TE/g) and chelating (19.81 ± 0.05 mg EDTAE/g) activities, respectively. Methanolic extract from HAE recorded the highest acetylcholinesterase (2.55 ± 0.10 mg GALAE/g) and α-amylase (0.51 ± 0.02 mmol ACAE/g) inhibition activities, while that obtained by maceration showed the best butyrylcholinesterase (3.76 ± 0.31 mg GALAE/g) inhibition activity. Both extracts revealed the best tyrosinase inhibitory activity (25.15 ± 1.00 and 26.79 ± 2.36 mg KAE/g, p ≥ 0.05). G. acutidentatum maceration-derived aqueous extract showed selective anticancer activity against cells originating from human hypopharyngeal carcinoma. In conclusion, these findings indicated that G. acutidentatum is a promising source of alkaloids and phenolic compounds for variable pharmaceutical formulations.
Food and Food Waste Antioxidants: Could They Be a Potent Defence against Parkinson’s Disease?
Authors: Cannas Claudia , Lostia Giada , Serra Pier Andrea , Peana Alessandra Tiziana , Migheli Rossana
Published: May 2024

Oxidative stress, an imbalance between reactive oxygen species (ROS) and endogenous antioxidants, plays an important role in the development of neurodegenerative diseases, including Parkinson’s. The human brain is vulnerable to oxidative stress because of the high rate of oxygen that it needs and the high levels of polyunsaturated fatty acids, which are substrates of lipid peroxidation. Natural antioxidants inhibit oxidation and reduce oxidative stress, preventing cancer, inflammation, and neurodegenerative disorders. Furthermore, in the literature, it is reported that antioxidants, due to their possible neuroprotective activity, may offer an interesting option for better symptom management, even Parkinson’s disease (PD). Natural antioxidants are usually found in several foods, such as fruits, vegetables, meat, fish, and oil, and in food wastes, such as seeds, peels, leaves, and skin. They can help the system of endogenous antioxidants, protect or repair cellular components from oxidative stress, and even halt lipid, protein, and DNA damage to neurons. This review will examine the extent of knowledge from the last ten years, about the neuroprotective potential effect of natural antioxidants present in food and food by-products, in in vivo and in vitro PD models. Additionally, this study will demonstrate that the pool of dietary antioxidants may be an important tool in the prevention of PD and an opportunity for cost savings in the public health area.
Deletion of Glyoxalase 1 Exacerbates Acetaminophen-Induced Hepatotoxicity in Mice
Authors: Dobariya Prakashkumar , Xie Wei , Rao Swetha Pavani , Xie Jiashu , Seelig Davis M. , Vince Robert , Lee Michael K. , More Swati S.
Published: May 2024

Acetaminophen (APAP) overdose triggers a cascade of intracellular oxidative stress events, culminating in acute liver injury. The clinically used antidote, N-acetylcysteine (NAC), has a narrow therapeutic window, and early treatment is essential for a satisfactory therapeutic outcome. For more versatile therapies that can be effective even at late presentation, the intricacies of APAP-induced hepatotoxicity must be better understood. Accumulation of advanced glycation end products (AGEs) and the consequent activation of the receptor for AGEs (RAGE) are considered one of the key mechanistic features of APAP toxicity. Glyoxalase 1 (Glo-1) regulates AGE formation by limiting the levels of methylglyoxal (MEG). In this study, we studied the relevance of Glo-1 in the APAP-mediated activation of RAGE and downstream cell death cascades. Constitutive Glo-1-knockout mice (GKO) and a cofactor of Glo-1, ψ-GSH, were used as tools. Our findings showed elevated oxidative stress resulting from the activation of RAGE and hepatocyte necrosis through steatosis in GKO mice treated with high-dose APAP compared to wild-type controls. A unique feature of the hepatic necrosis in GKO mice was the appearance of microvesicular steatosis as a result of centrilobular necrosis, rather than the inflammation seen in the wild type. The GSH surrogate and general antioxidant ψ-GSH alleviated APAP toxicity irrespective of the Glo-1 status, suggesting that oxidative stress is the primary driver of APAP toxicity. Overall, the exacerbation of APAP hepatotoxicity in GKO mice suggests the importance of this enzyme system in antioxidant defense against the initial stages of APAP overdose.
The Role of NRF2 in Trinucleotide Repeat Expansion Disorders
Authors: Chang Kuo-Hsuan , Chen Chiung-Mei
Published: May 2024

Trinucleotide repeat expansion disorders, a diverse group of neurodegenerative diseases, are caused by abnormal expansions within specific genes. These expansions trigger a cascade of cellular damage, including protein aggregation and abnormal RNA binding. A key contributor to this damage is oxidative stress, an imbalance of reactive oxygen species that harms cellular components. This review explores the interplay between oxidative stress and the NRF2 pathway in these disorders. NRF2 acts as the master regulator of the cellular antioxidant response, orchestrating the expression of enzymes that combat oxidative stress. Trinucleotide repeat expansion disorders often exhibit impaired NRF2 signaling, resulting in inadequate responses to excessive ROS production. NRF2 activation has been shown to upregulate antioxidative gene expression, effectively alleviating oxidative stress damage. NRF2 activators, such as omaveloxolone, vatiquinone, curcumin, sulforaphane, dimethyl fumarate, and resveratrol, demonstrate neuroprotective effects by reducing oxidative stress in experimental cell and animal models of these diseases. However, translating these findings into successful clinical applications requires further research. In this article, we review the literature supporting the role of NRF2 in the pathogenesis of these diseases and the potential therapeutics of NRF2 activators.
Ginkgo biloba: A Leaf of Hope in the Fight against Alzheimer’s Dementia: Clinical Trial Systematic Review
Authors: Pagotto Guilherme Lopes de Oliveira , Santos Livia Maria Oliveira dos , Osman Najwa , Lamas Caroline Barbalho , Laurindo Lucas Fornari , Pomini Karina Torres , Guissoni Leila M. , Lima Enzo Pereira de , Goulart Ricardo de Alvares , Catharin Virginia M. C. Strozze , Direito Rosa , Tanaka Masaru , Barbalho Sandra Maria
Published: May 2024

Alzheimer’s disease (AD) is a stealthy and progressive neurological disorder that is a leading cause of dementia in the global elderly population, imposing a significant burden on both the elderly and society. Currently, the condition is treated with medications that alleviate symptoms. Nonetheless, these drugs may not consistently produce the desired results and can cause serious side effects. Hence, there is a vigorous pursuit of alternative options to enhance the quality of life for patients. Ginkgo biloba (GB), an herb with historical use in traditional medicine, contains bioactive compounds such as terpenoids (Ginkgolides A, B, and C), polyphenols, organic acids, and flavonoids (quercetin, kaempferol, and isorhamnetin). These compounds are associated with anti-inflammatory, antioxidant, and neuroprotective properties, making them valuable for cognitive health. A systematic search across three databases using specific keywords—GB in AD and dementia—yielded 1702 documents, leading to the selection of 15 clinical trials for synthesis. In eleven studies, GB extract/EGb 761® was shown to improve cognitive function, neuropsychiatric symptoms, and functional abilities in both dementia types. In four studies, however, there were no significant differences between the GB-treated and placebo groups. Significant improvements were observed in scores obtained from the Mini-Mental State Examination (MMSE), Short Cognitive Performance Test (SKT), and Neuropsychiatric Inventory (NPI). While the majority of synthesized clinical trials show that Ginkgo biloba has promising potential for the treatment of these conditions, more research is needed to determine optimal dosages, effective delivery methods, and appropriate pharmaceutical formulations. Furthermore, a thorough assessment of adverse effects, exploration of long-term use implications, and investigation into potential drug interactions are critical aspects that must be carefully evaluated in future studies.
Health Benefits of Antioxidant Bioactive Compounds in Ginger (Zingiber officinale) Leaves by Network Pharmacology Analysis Combined with Experimental Validation
Authors: Nam Dong-Geon , Kim Mina , Choi Ae-Jin , Choe Jeong-Sook
Published: May 2024

Network pharmacology is an ideal tool to explore the effects of therapeutic components derived from plants on human metabolic diseases that are linked to inflammation. This study investigated the antioxidant effects of ginger leaves (GLs) and predicted targets for antioxidant activity. Quantitative and free radical scavenging analyses were performed to detect the main bioactive compounds of GLs and evaluate their antioxidant activities. Chemical diversity and network pharmacology approaches were used to predict key antioxidant components of GLs and their molecular targets. Nine major bioactive compounds of GLs were quantified using an internal standard method, and the antioxidant activity was evaluated using the DPPH and ABTS free radical scavenging methods. We first built the compound-gene-pathways and protein-protein interaction networks of GLs-related antioxidant targets and then conducted gene ontology and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analyses. Molecular docking results show that astragalin, a compound isolated from GLs, had the highest level of connectivity in the compound-target network and was involved in inflammation-related biosynthesis by directly impacting cytokine gene expression and PTGS2 inhibition markers. These findings not only suggest that the compounds isolated from GLs can be developed as potential antioxidants, but also demonstrate the applicability of network pharmacology to assess the potential of foods for disease treatment.
Pseudomonas aeruginosa Activates Quorum Sensing, Antioxidant Enzymes and Type VI Secretion in Response to Oxidative Stress to Initiate Biofilm Formation and Wound Chronicity
Authors: Kim Jane H. , Dong Julianna , Le Brandon H. , Lonergan Zachery R. , Gu Weifeng , Girke Thomas , Zhang Wei , Newman Dianne K. , Martins-Green Manuela
Published: May 2024

Pseudomonas aeruginosa (PA) is an opportunistic pathogen frequently isolated from cutaneous chronic wounds. How PA, in the presence of oxidative stress (OS), colonizes chronic wounds and forms a biofilm is still unknown. The purpose of this study is to investigate the changes in gene expression seen when PA is challenged with the high levels of OS present in chronic wounds. We used a biofilm-forming PA strain isolated from the chronic wounds of our murine model (RPA) and performed a qPCR to obtain gene expression patterns as RPA developed a biofilm in vitro in the presence of high levels of OS, and then compared the findings in vivo, in our mouse model of chronic wounds. We found that the planktonic bacteria under OS conditions overexpressed quorum sensing genes that are important for the bacteria to communicate with each other, antioxidant stress genes important to reduce OS in the microenvironment for survival, biofilm formation genes and virulence genes. Additionally, we performed RNAseq in vivo and identified the activation of novel genes/pathways of the Type VI Secretion System (T6SS) involved in RPA pathogenicity. In conclusion, RPA appears to survive the high OS microenvironment in chronic wounds and colonizes these wounds by turning on virulence, biofilm-forming and survival genes. These findings reveal pathways that may be promising targets for new therapies aimed at disrupting PA-containing biofilms immediately after debridement to facilitate the treatment of chronic human wounds.
A Deep Learning Method to Mitigate the Impact of Subjective Factors in Risk Estimation for Machinery Safety
Authors: Zhu Xiaopeng , Wang Aiguo , Zhang Ke , Hua Xueming
Published: May 2024

Risk estimation holds significant importance in the selection of risk reduction measures and ensuring machinery safety. However, subjective influences of assessors lead to an inconsistent understanding of risk among relevant stakeholders, hindering the achievement of safety. As similarities exists in product updates or applications in engineering practice, the historical risk information of similar products or applications has essential application value. A novel deep learning approach was established to estimate risks based on historical risk information. To address the issue of overfitting caused by a limited dataset, a data augmentation technique was employed. Our experiment was conducted on the raw, 2×, and 6× hazard event dataset of an industrial robot, demonstrating a substantial improvement in both accuracy and stability. On the validation dataset, there was an increase in median accuracy from 55.56% to 96.92%, with a decrease in standard deviation from 0.118 to 0.015. On the new dataset, the trained network also showed near-perfect performance on similar hazard events and trustworthiness on completely different ones. In cases of risk deviations, approximately 80% of them were small deviations (|RIdeviation| ≤ 2) without a noticeable bias (RIdis is close to 1). The LSTM-based deep learning network makes risk estimation “black-boxed” and “digitized”. Assessors just need to focus on hazard identification with risk being determined by the trained network, mitigating the impact of individual factors. Moreover, the historical risk estimation information can be transformed into a trained network, facilitating the development of a standardized benchmark within project teams, enterprises, and relevant stakeholders to promote coordinated safety measures.
Special Issue on Recent Advances and Future Trends in Nanophotonics II
Authors: Ferrara Maria Antonietta , Dardano Principia
Published: May 2024

Nanophotonics, at the forefront of science and engineering, intrigues researchers across disciplines with its transformative potential for diverse technologies [...]
Universal Local Attractors on Graphs
Authors: Krasanakis Emmanouil , Papadopoulos Symeon , Kompatsiaris Ioannis
Published: May 2024

Being able to express broad families of equivariant or invariant attributed graph functions is a popular measuring stick of whether graph neural networks should be employed in practical applications. However, it is equally important to find deep local minima of losses (i.e., produce outputs with much smaller loss values compared to other minima), even when architectures cannot express global minima. In this work we introduce the architectural property of attracting optimization trajectories to local minima as a means of achieving smaller loss values. We take first steps in satisfying this property for losses defined over attributed undirected unweighted graphs with an architecture called universal local attractor (ULA). This refines each dimension of end-to-end-trained node feature embeddings based on graph structure to track the optimization trajectories of losses satisfying some mild conditions. The refined dimensions are then linearly pooled to create predictions. We experiment on 11 tasks, from node classification to clique detection, on which ULA is comparable with or outperforms popular alternatives of similar or greater theoretical expressive power.
Polyethylene Terephthalate Fiber Modified with Type I Collagen as a 3D Scaffold Material for Bioartificial Liver
Authors: Li Yang , Zhang Yang , Gao Jianping , Liao Shuguang , Zhang Guifeng
Published: May 2024

Acute and chronic liver failure are clinically significant conditions, and the artificial liver support system (ALSS) is emerging as a novel and effective approach for the clinical management of liver failure. Within this framework, scaffold materials occupy a pivotal position as integral components of the bioreactor. Elevating the performance capabilities of these scaffolds not only augments the therapeutic efficacy of the artificial liver but also lays the groundwork for refining and selecting large-scale hepatocyte culture models. In this study, we introduced a novel hepatocyte scaffold material designated as PET-COL, crafted by coating polyethylene terephthalate (PET) with collagen. This involved a sequence of modifications, including alkaline hydrolysis, EDC/NHS activation and crosslinking, as well as collagen conjugation. The physicochemical attributes of the scaffold were thoroughly characterized by Fourier-transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), second harmonic generation (SHG), water contact angle measurements, and high-performance liquid chromatography–mass spectrometry (HPLC-MS). Furthermore, an investigation into the material’s biological properties was conducted that encompassed SEM (HepaRG growth), fluorescence staining (assessment of cell viability), staining by trypan blue (HepaRG counting), CCK-8 (proliferation of cells), biochemical testing, and immunosorbent assay. Our findings revealed that collagen was covalently bonded to the PET surface, leading to a substantial enhancement in the material’s hydrophilicity (p < 0.001). The quantity of collagen coating was determined to be precisely 33.30 μg per scaffold. Human liver progenitor HepaRG cells thrived on the PET-COL material. Compared with the untreated group, cell viability, albumin secretion, urea synthesis, and the expression levels of CYP3A4 and CPS1 increased significantly (p < 0.001), demonstrating remarkable biological vitality. The PET-COL scaffold, as developed in this study, holds immense potential for application in bioartificial livers.
Integrating Modular Design Concepts for Enhanced Efficiency in Digital and Sustainable Manufacturing: A Literature Review
Authors: Roy Marc-Antoine , Abdul-Nour Georges
Published: May 2024

Small- and medium-sized manufacturing enterprises (SMMEs) face intense competitiveness, necessitating ever greater productivity. Enterprises struggle to meet the demand for customized products while maintaining their productivity. The transition from mass customization (MC) to mass personalization (MPe) leads to a further increase in product variety and, thus, complexity. Digital transformation alone is not sufficient to achieve MPe and traditional adoption of modularity no longer ensures enterprise competitiveness in this context of increased variety. The synergy between modularity concepts could enhance the efficiency of this design strategy. This study is part of a research plan to develop an effective modularity implementation strategy addressing MPe. The aim of this article is to identify the main concepts and tools to be considered in an implementation strategy. Concepts and tools are grouped into four combinations according to the level of product variety in different production strategies. This preliminary work serves as the foundational research for a larger research plan aimed at adapting and validating a modular product development strategy that incorporates these modularity concepts.
Software Architectures for Adaptive Mobile Learning Systems: A Systematic Literature Review
Authors: Ruiz Nepomuceno Aldair , López Domínguez Eduardo , Domínguez Isidro Saúl , Medina Nieto María Auxilio , Meneses-Viveros Amilcar , de la Calleja Jorge
Published: May 2024

Adaptive Mobile Learning Systems (AMLSs) are technological tools that incorporate adaptive learning to generate an individual teaching–learning process for each student. Despite the proliferation of AMLS, their development is often hindered by a lack of standardization and the poor quality of existing software architectures in terms of requirements correctness and coverage. This paper presents a systematic literature review (SLR) of software architectures oriented to developing AMLS reported in the state of the art. For this SLR, we searched the ACM Digital Library, IEEE Xplore, Science Direct, Springer, and SCOPUS. Studies published in English that describe and evaluate a software architecture to develop AMLS were included. Twenty-two primary works were relevant to the present study. Based on the obtained results, we have identified key aspects that must be considered in a software architecture proposed for developing an AMLS in terms of requirements, quality attributes, stakeholders, services provided to users, views and architectural styles, components, and implementation technologies. The main finding of this work is the lack of a reference software architecture that considers all the requirements and quality attributes needed for developing AMLS. This gap hinders the effective development and standardization of quality AMLSs, suggesting a critical need for a comprehensive reference software architecture.
Dynamic Responses and Failure Characteristics of Deep Double U-Shaped Caverns under Disturbing Loads
Authors: Liang Lisha , Li Xibing , Liu Zhixiang
Published: May 2024

The instability of double-cavern structure subjected to dynamic disturbances is a key issue for deep rock engineering. To investigate the dynamic responses of deep double U-shaped caverns, comprehensive analyses are conducted by Particle Flow Code (PFC2D), and the influences of incident directions of stress wave, cavern clearances, and cavern height ratios are discussed. The results indicate that the decreasing cavern clearance aggravates the static stress concentration on the intermediate rock pillar. When the stress wave is horizontally incident, the presence of the incident side cavern reduces peak tangential stress and kinetic energy on the non-incident side cavern; the higher the incident side cavern, the less damage on the non-incident side cavern. A vertically incident stress wave causes more severe damage in the intermediate rock pillar compared to a horizontally incident stress wave; the smaller the cavern clearance, the more violent the rockburst in the intermediate rock pillar. Comparatively, the cavern with a lower height exhibits more severe failure at the adjacent sidewall compared to the cavern with a higher height. This work can provide guidelines for disaster prevention of deep double-cavern structures.
Mechanism of Surface Hydroxylation Acceleration and Laser-Induced Damage Threshold Reduction during Ion Beam Sputtering of Fused Silica
Authors: Xu Mingjin , Wu Weibin , Ke Yongsheng , Liu Xiaohong , Zhong Yaoyu , Gao Xiaopeng
Published: May 2024

The mechanism of the combined process of ion beam sputtering (IBS) and HF acid etching on the chemical structure defects of fused silica and its laser damage resistance performance were investigated in this paper. During the removal process of surface material, the sputtering effect causes lattice atoms to flee their native space locations, and a large amount of unsaturated chemical structures are produced on the silica surface, which improves the chemical activity of Si and O atoms, accelerates the chemical reaction process between surface atoms and water molecules, increases the content of hydroxyl groups (OH-) in the shallow layer, and enhances the photothermal weak absorption intensity. However, the increase in hydroxyl content weakens the binding strength of silicon–oxygen bonds, destroys the spatial network structure of silica bulk, and reduces its mechanical strength, resulting in a decrease in its laser damage resistance performance. The paper reveals for the first time the mechanism by which IBS changes the structure characteristics of silica material, accelerates the surface hydroxylation process, and thereby reduces the laser damage resistance performance. This work provides technical guidance for effectively suppressing chemical structure defects on silica surfaces and improving the laser damage resistance performance of optical components under high-flux laser irradiation.
Advancements in Gas Turbine Fault Detection: A Machine Learning Approach Based on the Temporal Convolutional Network–Autoencoder Model
Authors: Fahmi Al-Tekreeti Watban Khalid , Reza Kashyzadeh Kazem , Ghorbani Siamak
Published: May 2024

To tackle the complex challenges inherent in gas turbine fault diagnosis, this study uses powerful machine learning (ML) tools. For this purpose, an advanced Temporal Convolutional Network (TCN)–Autoencoder model was presented to detect anomalies in vibration data. By synergizing TCN capabilities and Multi-Head Attention (MHA) mechanisms, this model introduces a new approach that performs anomaly detection with high accuracy. To train and test the proposed model, a bespoke dataset of CA 202 accelerometers installed in the Kirkuk power plant was used. The proposed model not only outperforms traditional GRU–Autoencoder, LSTM–Autoencoder, and VAE models in terms of anomaly detection accuracy, but also shows the Mean Squared Error (MSE = 1.447), Root Mean Squared Error (RMSE = 1.193), and Mean Absolute Error (MAE = 0.712). These results confirm the effectiveness of the TCN–Autoencoder model in increasing predictive maintenance and operational efficiency in power plants.
Application of Natural Edible Coating to Enhance the Shelf Life of Red Fruits and Their Bioactive Content
Authors: Pellegrino Michele , Elechi Jasper Okoro Godwin , Plastina Pierluigi , Loizzo Monica Rosa
Published: May 2024

Red fruits contain bioactive substances including phenolic acids and flavonoids, which provide many health advantages for the human body. Industries find them intriguing because of their color and their ability to prevent chronic ailments such as metabolic, degenerative, and cardiovascular disorders. Nevertheless, the resilience of these organic molecules is influenced by several environmental, physical, and chemical phenomena. Therefore, the beneficial health properties of red fruits may diminish during postharvest processing. In this scenario, many postharvest methods have been implemented to enhance the shelf life and preserve the bioactive components of red fruits. The objectives of this review were to provide a comprehensive assessment of the health benefits of red fruits, and to explore the possibilities of edible coatings in retaining their freshness and protecting their bioactive contents. Co-occurrence networks were built using VOSviewer software to produce a two-dimensional map based on term frequency, and the examination of the 1364 keywords obtained from the scientific papers revealed the presence of at least 71 co-occurrences that provide insight into many natural components used in edible coatings for red fruits, such as proteins, polysaccharides, lipids, phospholipids, and minerals. The review examined their composition, functioning, application techniques, limits, safety considerations, legal regulations, and potential future developments. This review has shown that an edible coating may act as a protective layer on the surface of the fruit, alter the interior gas composition, reduce water loss, and postpone fruit ripening, thereby enhancing the health-promoting properties.
Graph Stream Compression Scheme Based on Pattern Dictionary Using Provenance
Authors: Lee Hyeonbyeong , Shin Bokyoung , Choi Dojin , Lim Jongtae , Bok Kyoungsoo , Yoo Jaesoo
Published: May 2024

With recent advancements in network technology and the increasing popularity of the internet, the use of social network services and Internet of Things devices has flourished, leading to a continuous generation of large volumes of graph stream data, where changes, such as additions or deletions of vertices and edges, occur over time. Additionally, owing to the need for the efficient use of storage space and security requirements, graph stream data compression has become essential in various applications. Even though various studies on graph compression methods have been conducted, most of them do not fully reflect the dynamic characteristics of graph streams and the complexity of large graphs. In this paper, we propose a compression scheme using provenance data to efficiently process and analyze large graph stream data. It obtains provenance data by analyzing graph stream data and builds a pattern dictionary based on this to perform dictionary-based compression. By improving the existing dictionary-based graph compression methods, it enables more efficient dictionary management through tracking pattern changes and evaluating their importance using provenance. Furthermore, it considers the relationships among sub-patterns using an FP-tree and performs pattern dictionary management that updates pattern scores based on time. Our experiments show that the proposed scheme outperforms existing graph compression methods in key performance metrics, such as compression rate and processing time.
Dynamic Land-Use Patterns and the Associated Impacts on Ecosystem Services Value in Putian City, China
Authors: Peng Qingxia , Wu Dongqing , Lin Wenxiong , Fan Shuisheng , Su Kai
Published: May 2024

Human actions have led to consistent and profound alterations in land use, which in turn have had a notable effect on the services provided by ecosystems. In this research, the Google Earth Engine (GEE) was initially employed to perform a supervised classification of Landsat satellite images from 2000 to 2020, which allowed us to obtain land-use data for Putian City, China. Next, the geo-informatic Tupu model and the revised valuation model were used to explore the spatial attributes and ecological effects of land-use changes (LUCs). Subsequently, EEH (eco-economic harmony), ESTD (ecosystem services tradeoffs and synergies degree index), and ESDA (exploratory spatial data analysis) methods were employed to further analyze the coordination level, trade-offs, synergies, and spatial patterns of ecological-economic system development. The findings revealed that: (1) The land-use composition in Putian City was predominantly cultivated land and forest land, with other types of land intermixed. Concurrently, there was an ongoing trend of expansion in urban areas. (2) ESV in Putian City exhibited an upward trend, increasing from 15.4 billion CNY to 23.1 billion CNY from 2000 to 2020. (3) ESV exhibited an imbalance in spatial distribution, with high-high agglomeration areas concentrated in the central part of Putian City and the coastal region of Hanjiang District, while low-low agglomeration areas were prevalent in Xianyou County in the southwest, Xiuyu District along the coast, and Licheng District in the urban center. (4) Synergistic relationships among ESs predominated, though the trade-off relationship showed a tendency to expand. (5) The ecological environment and economic progress in Putian City collectively faced a region of potential risk. The findings of this study are intended to serve as a guide for improving the distribution of land resources and for developing strategies that ensure the sustainable development of the region’s socio-economic framework.
ADLBiLSTM: A Semantic Generation Algorithm for Multi-Grammar Network Access Control Policies
Authors: Zhang Jing , Liang Xiaoyan
Published: May 2024

Semantic generation of network access control policies can help network administrators accurately implement policies to achieve desired security objectives. Current semantic generation research mainly focuses on semantic generation of single grammar and lacks work on automatically generating semantics for different grammatical strategies. Generating semantics for different grammars is a tedious, inefficient, and non-scalable task. Inspired by sequence labeling in the field of natural language processing, this article models automatic semantic generation as a sequence labeling task. We propose a semantic generation algorithm named ADLBiLSTM. The algorithm uses a self-attention mechanism and double-layer BiLSTM to extract the features of security policies from different aspects, so that the algorithm can flexibly adapt to policies of different complexity without frequent modification. Experimental results showed that the algorithm has good performance and can achieve high accuracy in semantic generation of access control list (ACL) and firewall data and can accurately understand and generate the semantics of network access control policies.
Machine Learning-Based Fatigue Level Prediction for Exoskeleton-Assisted Trunk Flexion Tasks Using Wearable Sensors
Authors: Kuber Pranav Madhav , Kulkarni Abhineet Rajendra , Rashedi Ehsan
Published: May 2024

Monitoring physical demands during task execution with exoskeletons can be instrumental in understanding their suitability for industrial tasks. This study aimed at developing a fatigue level prediction model for Back-Support Industrial Exoskeletons (BSIEs) using wearable sensors. Fourteen participants performed a set of intermittent trunk-flexion task cycles consisting of static, sustained, and dynamic activities, until they reached medium-high fatigue levels, while wearing BSIEs. Three classification algorithms, Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB), were implemented to predict perceived fatigue level in the back and leg regions using features from four wearable wireless Electromyography (EMG) sensors with integrated Inertial Measurement Units (IMUs). We examined the best grouping and sensor combinations by comparing prediction performance. The findings showed best performance in binary classification of leg and back fatigue with 95% (2 EMG + IMU sensors) and 82% (single IMU sensor) accuracy, respectively. Tertiary classification for back and leg fatigue level prediction required four sensor setups with both EMG and IMU measures to perform at 79% and 67% accuracy, respectively. The efforts presented in our article demonstrate the feasibility of an accessible fatigue level detection system, which can be beneficial for objective fatigue assessment, design selection, and implementation of BSIEs in real-world scenarios.
Removal of Color-Document Image Show-Through Based on Self-Supervised Learning
Authors: Ni Mengying , Liang Zongbao , Xu Jindong
Published: May 2024

Show-through phenomena have always been a challenging issue in color-document image processing, which is widely used in various fields such as finance, education, and administration. Existing methods for processing color-document images face challenges, including dealing with double-sided documents with show-through effects, accurately distinguishing between foreground and show-through parts, and addressing the issue of insufficient real image data for supervised training. To overcome these challenges, this paper proposes a self-supervised-learning-based method for removing show-through effects in color-document images. The proposed method utilizes a two-stage-structured show-through-removal network that incorporates a double-cycle consistency loss and a pseudo-similarity loss to effectively constrain the process of show-through removal. Moreover, we constructed two datasets consisting of different show-through mixing ratios and conducted extensive experiments to verify the effectiveness of the proposed method. Experimental results demonstrate that the proposed method achieves competitive performance compared to state-of-the-art methods and can effectively perform show-through removal without the need for paired datasets. Specifically, the proposed method achieves an average PSNR of 33.85 dB on our datasets, outperforming comparable methods by a margin of 0.89 dB.