bluuebunny/crossref_metadata_2025 huggingface dataset:
Antioxidant Activities of Ethanolic Extracts Obtained from α-Pinene-Containing Plants and Their Use in Cosmetic Emulsions
Authors: Grzeszczak Jadwiga , Wróblewska Agnieszka , Klimowicz Adam , Gajewska Sylwia , Kucharski Łukasz , Koren Zvi C. , Janda-Milczarek Katarzyna
Published: July 2024

α-Pinene is the bicyclic, unsaturated terpene hydrocarbon present in many plants. Due to its beneficial chemical properties, this compound is of great interest and has found numerous applications as a raw material in many chemical industries as well as in medicine and cosmetics. The aim of this study was to evaluate the antioxidant activities of ethanolic extracts obtained from plants containing α-pinene and to test the properties of cosmetic emulsions prepared with these extracts. The raw plant materials consisted of fresh parts of Pinus sylvestris L., such as cones, needles, and branches, as well as dried unground and ground pinecones; dried and fresh Rosmarinus officinalis leaves; dried Levisticum officinale leaves; and dried Salvia officinalis L. leaves. The plant materials were individually extracted with 40% (v/v), 70% (v/v), and 96% (v/v) ethanol using ultrasound-assisted extraction (UAE) for 15, 30, or 60 min. This method is a green extraction technique, frequently applied to isolate active substances from plants. For the selected plant materials, Soxhlet extraction with 96% (v/v) ethanol was also performed. The qualitative and quantitative analyses of the components in the selected extracts were performed with gas chromatography coupled with mass spectrometry (GC-MS). The antioxidant activities of the extracts were evaluated with the DPPH and ABTS methods. The extracts of three plant materials with the highest antioxidant activities—dried Rosmarinus officinalis leaves, dried Salvia officinalis L. leaves, and dried and ground Pinus sylvestris L. cones—were selected to be incorporated in cosmetic emulsions containing glyceryl monostearate and Olivem 1000 as emulsifiers. The stabilities and antioxidant activities of the emulsions were evaluated. Moreover, the antimicrobial properties of the emulsions using microbiological tests were also determined. The findings suggest that the prepared emulsions are stable cosmetic products with a high antioxidant potential.
Kinetic Insights into the Antioxidant Effect of Isatin-Thiosemicarbazone in Biodiesel Blends
Authors: Karakullukçu Nalan Türköz , Muğlu Halit , Yakan Hasan , Yılmaz Volkan Murat , Marah Sarmad , İnce İkbal Agah
Published: July 2024

Biodiesel has several drawbacks, such as being prone to oxidation, having reduced stability, and having limited storage time. Antioxidants compatible with biodiesel are being used to address its drawbacks. Utilizing antioxidants effectively improves the quality of biodiesel. Enhancing the quality of biodiesel for use as a clean energy source benefits both the global economy and ecology. Therefore, we believe that our work will contribute to the advancement of the biodiesel industry worldwide. This study used blends consisting of 20% biodiesel and 80% diesel fuel. Isatin-thiosemicarbazones were tested as additives in blends at a concentration of 3000 parts per million (ppm) using an oxifast device and were compared with the chemical antioxidant Trolox. FT-IR, DSC, and TGA were used to characterize these samples. DSC measured sample crystallization temperatures (Tc). Samples with antioxidants showed decreased values compared to the non-antioxidant diesel sample D100. Several DSC tests were conducted to determine the antioxidant strengths of various samples. The results show that the FT-IR spectrum’s antioxidant effect regions grow clearer with antioxidants. The extra antioxidant is effective. Biodiesel’s oxidative stability improves with isatin-thiosemicarbazones at varying concentrations. The kinetics of thermal decomposition of isatin-thiosemicarbazones under non-isothermal conditions were determined using the Kissinger, Ozawa, and Boswell techniques. The activation energies of compounds 1 and 2 were calculated as 137–147 kJ mol−1 and 173–183 kJ mol−1, respectively.
A Blockchain-Driven Smart Broker for Data Quality Assurance of the Tagged Periodic IoT Data in Publisher-Subscriber Model
Authors: Idrees Rabbia , Maiti Ananda
Published: July 2024

The Publisher-Subscriber model of data exchange has been a popular method for many Internet-based applications, including the Internet of Things (IoT). A traditional PS system consists of publishers, subscribers, and a broker. The publishers create new data for a registered topic, and the data broker relays the data to the corresponding subscribers. This paper introduces a blockchain-based smart broker for the publisher-subscriber (PS) framework for the IoT network. As IoT data comes from devices operating in various environments, it may suffer from multiple challenges, such as hardware failures, connectivity issues, and external vulnerabilities, thereby impacting data quality in terms of accuracy and timeliness. It is important to monitor this data and inform subscribers about its quality. The proposed smart broker is composed of multiple smart contracts that continuously monitor the quality of the topic data by assessing its relationship with other related topics and its drift or delay in publishing intervals. It assigns a reputation score to each topic computed based on its quality and drifts, and it passes both the original data and the reputation score as a measure of quality to the subscriber. Furthermore, the smart broker can suggest substitute topics to subscribers when the requested topic data are unavailable or of very poor quality. The evaluation shows that a smart broker efficiently monitors the reputation of the topic data, and its efficiency increases notably when the data quality is worse. As the broker is run inside the blockchain, it automatically inherits the advantages of the blockchain, and the quality scoring is indisputable based on immutable data.
Impact of Hyperparameter Optimization to Enhance Machine Learning Performance: A Case Study on Breast Cancer Recurrence Prediction
Authors: González-Castro Lorena , Chávez Marcela , Duflot Patrick , Bleret Valérie , Del Fiol Guilherme , López-Nores Martín
Published: July 2024

Accurate and early prediction of breast cancer recurrence is crucial to guide medical decisions and treatment success. Machine learning (ML) has shown promise in this domain. However, its effectiveness critically depends on proper hyperparameter setting, a step that is not always performed systematically in the development of ML models. In this study, we aimed to highlight the impact that this process has on the final performance of ML models through a real-world case study by predicting the five-year recurrence of breast cancer patients. We compared the performance of five ML algorithms (Logistic Regression, Decision Tree, Gradient Boosting, eXtreme Gradient Boost, and Deep Neural Network) before and after optimizing their hyperparameters. Simpler algorithms showed better performance using the default hyperparameters. However, after the optimization process, the more complex algorithms demonstrated superior performance. The AUCs obtained before and after adjustment were 0.7 vs. 0.84 for XGB, 0.64 vs. 0.75 for DNN, 0.7 vs. 0.8 for GB, 0.62 vs. 0.7 for DT, and 0.77 vs. 0.72 for LR. The results underscore the critical importance of hyperparameter selection in the development of ML algorithms for the prediction of cancer recurrence. Neglecting this step can undermine the potential of more powerful algorithms and lead to the choice of suboptimal models.
Optimizing Mobile Vision Transformers for Land Cover Classification
Authors: Rozario Papia F. , Gadgil Ravi , Lee Junsu , Gomes Rahul , Keller Paige , Liu Yiheng , Sipos Gabriel , McDonnell Grace , Impola Westin , Rudolph Joseph
Published: July 2024

Image classification in remote sensing and geographic information system (GIS) data containing various land cover classes is essential for efficient and sustainable land use estimation and other tasks like object detection, localization, and segmentation. Deep learning (DL) techniques have shown tremendous potential in the GIS domain. While convolutional neural networks (CNNs) have dominated image analysis, transformers have proven to be a unifying solution for several AI-based processing pipelines. Vision transformers (ViTs) can have comparable and, in some cases, better accuracy than a CNN. However, they suffer from a significant drawback associated with the excessive use of training parameters. Using trainable parameters generously can have multiple advantages ranging from addressing model scalability to explainability. This can have a significant impact on model deployment in edge devices with limited resources, such as drones. In this research, we explore, without using pre-trained weights, how the inherent structure of vision transformers behaves with custom modifications. To verify our proposed approach, these architectures are trained on multiple land cover datasets. Experiments reveal that a combination of lightweight convolutional layers, including ShuffleNet, along with depthwise separable convolutions and average pooling can reduce the trainable parameters by 17.85% and yet achieve higher accuracy than the base mobile vision transformer (MViT). It is also observed that utilizing a combination of convolution layers along with multi-headed self-attention layers in MViT variants provides better performance for capturing local and global features, unlike the standalone ViT architecture, which utilizes almost 95% more parameters than the proposed MViT variant.
Integrating Shapley Values into Machine Learning Techniques for Enhanced Predictions of Hospital Admissions
Authors: Feretzakis Georgios , Sakagianni Aikaterini , Anastasiou Athanasios , Kapogianni Ioanna , Bazakidou Effrosyni , Koufopoulos Petros , Koumpouros Yiannis , Koufopoulou Christina , Kaldis Vasileios , Verykios Vassilios S.
Published: July 2024

(1) Background: Predictive modeling is becoming increasingly relevant in healthcare, aiding in clinical decision making and improving patient outcomes. However, many of the most potent predictive models, such as deep learning algorithms, are inherently opaque, and their decisions are challenging to interpret. This study addresses this challenge by employing Shapley Additive Explanations (SHAP) to facilitate model interpretability while maintaining prediction accuracy. (2) Methods: We utilized Gradient Boosting Machines (GBMs) to predict patient outcomes in an emergency department setting, with a focus on model transparency to ensure actionable insights. (3) Results: Our analysis identifies “Acuity”, “Hours”, and “Age” as critical predictive features. We provide a detailed exploration of their intricate interactions and effects on the model’s predictions. The SHAP summary plots highlight that “Acuity” has the highest impact on predictions, followed by “Hours” and “Age”. Dependence plots further reveal that higher acuity levels and longer hours are associated with poorer patient outcomes, while age shows a non-linear relationship with outcomes. Additionally, SHAP interaction values uncover that the interaction between “Acuity” and “Hours” significantly influences predictions. (4) Conclusions: We employed force plots for individual-level interpretation, aligning with the current shift toward personalized medicine. This research highlights the potential of combining machine learning’s predictive power with interpretability, providing a promising route concerning a data-driven, evidence-based healthcare future.
GenTrajRec: A Graph-Enhanced Trajectory Recovery Model Based on Signaling Data
Authors: Huang Hongyao , Xie Haozhi , Xu Zihang , Liu Mingzhe , Xu Yi , Zhu Tongyu
Published: July 2024

Signaling data are records of the interactions of users’ mobile phones with their nearest cellular stations, which could provide long-term and continuous-time location data of large-scale citizens, and therefore have great potential in intelligent transportation, smart cities, and urban sensing. However, utilizing the raw signaling data often suffers from two problems: (1) Low positioning accuracy. Since the signaling data only describes the interaction between the user and the mobile base station, they can only restore users’ approximate geographical location. (2) Poor data quality. Due to the limitations of mobile signals, user signaling may be missing and drifting. To address the above issues, we propose a graph-enhanced trajectory recovery network, GenTrajRec, to recover precise trajectories from signaling data. GenTrajRec encodes signaling data through spatiotemporal encoders and enhances the traveling semantics by constructing a signaling transition graph. In fusing the spatiotemporal information as well as the deep traveling semantics, GenTrajRec can well tackle the challenge of poor data quality, and recover precise trajectory from raw signaling data. Extensive experiments have been conducted on two real-world datasets from Mobile Signaling and Geolife, and the results confirm the effectiveness of our approach, and the positioning accuracy can be improved from 315 m per point to 82 m per point for signaling data using our network.
Exploring Recycled Polyethylene Terephthalate (PET) Based Cushioning Design to Reduce Bruise Damage in Pears
Authors: Mecheter Asma , Tarlochan Faris , Pathare Pankaj B.
Published: July 2024

Post-harvest activities, which include sorting, loading, unloading, and transporting, are potential factors that cause mechanical damage and bruises to fresh produce. This would directly impact fruit shelf-life and, therefore, cause economic losses. This study developed a finite element (FE) model for pear fruit where a steel impactor drop-based test was utilized. The FE model was validated by evaluating it as the experimental model in order to identify bruises of the pear fruit. Therefore, to minimize bruises on the pear fruit, a recycled polyethylene terephthalate (PET) spring-based design was proposed in order to serve as a cushioning design for pear fruits. Design of experiments and response surface methodology were performed in order to minimize the fruit bruise susceptibility response subject to different spring design parameters. The results revealed that reduced spring pitch and increased coil thickness would significantly minimize bruises of pear fruit. The recycled PET proposed design proved its efficiency in reducing FE pear fruit model bruises by about 50%. This study provides insights on assessing bruise susceptibility using finite element analysis and reusing plastic for fresh produce packaging, thus reducing loops in supply chains and achieving a circular economy.
On the Analysis of Coverage Feedback in a Fuzzing Proprietary System
Authors: Jang Daehee , Kim Jaemin , Kim Jiho , Im Woohyeop , Jeong Minwoo , Choi Byeongcheol , Kil Chongkyung
Published: July 2024

Coverage feedback is one of the key mechanisms for improving the effectiveness of fuzzers by measuring and comparing the executed code regions while processing input data. In general, such guidance should always improve the performance of fuzzers to better find unexplored code regions. However, proprietary systems with uncommon I/O interfaces (e.g., UAV system, IoT devices, satellite firmware) require extensive engineering/porting efforts to apply coverage feedback support in developing their fuzzing platform. In this paper, we evaluate the detailed efficacy of coverage feedback in fuzzing based on 44 real-world bugs we found using OSS-Fuzz. Our analysis uncovered when and how code coverage information can be helpful, and our experiment demonstrates that although coverage guidance is always helpful to some extent, its effectiveness depends on various external factors. Therefore, such factors should be carefully considered for optimizing the cost and efficiency in designing the fuzzing architecture of proprietary systems.
Comparative Analysis of Tribological Behavior of 45 Steel under Intensive Quenching-High-Temperature Tempering and Queenching-Tempering Process
Authors: Su Xiyang , Zhu Xiaoshuo , Fu Yudong , Xiao Shichen , Liu Yuchao
Published: July 2024

The intensive quenching process compared to traditional methods results in a lower quenching cracking tendency. The comprehensive mechanical properties of an intensive quenching workpiece has good advantages. In order to improve the performance and product quality of a 45 steel workpiece, the hardening–tempering treatment used in the traditional quenching process is replaced by an intensive quenching process. This study investigates the tribological properties of 45 steel and their differences and connection under the intensive quenching and high-temperature tempering process in comparison to when under the traditional hardening–tempering process. Both intensive quenching and tempering and hardening–tempering workpieces are composed of carburized particles and ferrite. Compared with hardening–tempering workpieces, intensive quenching and high-temperature tempering workpieces have a finer and more uniform microstructure and higher hardness, impact toughness, and yield strength. Wear tests show that intensive quenchingand tempered specimens have better wear resistance. At the same frequency, the coefficient of friction and relative wear rate of the intensive quenching and tempering specimens were lower than those of the hardening–tempering treatment, and the wear surface was flatter. The wear morphology shows that the main wear mechanisms of the intensive quenching and tempering workpieces and those of hardening–tempering are abrasive and adhesive wear, and that the main wear mechanism changes from adhesive wear to abrasive wear as the frequency increases.
Propagation Characteristics of Initial Compression Wave Induced by 400 km/h High-Speed Trains Passing through Very Long Tunnels
Authors: Wei Kang , Mei Yuangui , Sun Qi , Hu Xiao
Published: July 2024

When high-speed trains enter tunnels, an initial compression wave is generated. As the compression wave propagates at the local speed of sound to the tunnel exit, it radiates into the surrounding environment, forming micro-pressure waves (MPWs). MPWs create sonic booms, resulting in significant environmental issues. The magnitude of the micro-pressure waves is directly proportional to the pressure gradient of the compression wave at the tunnel exit. The nonlinear effects of the initial compression wave during propagation lead to a significant increase in pressure gradient. Therefore, the propagation characteristics of the initial compression wave during the tunnel are the crucial factor affecting the amplitude of MPWs. Based on the one-dimensional compressible unsteady non-isentropic flow model and the improved generalized Riemann variable characteristic method, this paper researched the propagation and evolution characteristics of an initial compression wave generated when 400 km/h high-speed trains enter tunnels with three portal shapes: (no tunnel entrance hood (no hood), an oblique, enlarged tunnel entrance hood (type A), an enlarged equal-section non-uniform opening hole tunnel entrance hood (type B)). The results show that when the initial compression wave propagates inside very long tunnels, the pressure gradient of the compression wave exhibits a trend of initially increasing and then decreasing with the increase in propagation distance. When the pressure gradient of the compression wave reaches its maximum value, the corresponding propagation distance is the steepening critical distance. For no tunnel entrance hoods, type A tunnel entrance hoods, and type B tunnel entrance hoods, the steepening critical distances are 5 km, 6 km, and 16 km, respectively. The steepening critical distance shortens with increasing train speed. Steady friction and unsteady friction effects mainly affect the pressure amplitude and pressure gradient during compression wave propagation, respectively. At lower ambient temperatures, the nonlinear effects in compression wave propagation are significantly enhanced. The mitigation effects of type A tunnel entrance hoods and type B tunnel entrance hoods on pressure gradient reduction are mainly concentrated within 4 km and 12 km, respectively. It is necessary to determine the optimal matching relationship between the tunnel entrance hood and tunnel length based on the characteristics of compression wave propagation to ensure their mitigating performance is maximized.
Multi-Factor Highway Freight Volume Prediction Based on Backpropagation Neural Network
Authors: Zhang Yanshuang , Tian Caixia , Guo Baohua , Wang Meixia , Zhang Zhezhe , Morobeni Kgaugelo
Published: July 2024

With the development of the times, the traditional single-factor time series prediction cannot meet the needs of actual prediction, and it is necessary to comprehensively consider the influence of various variables on prediction results. Therefore, we use MATLAB R2022a to predict the multi-factor highway freight volume. According to the relevant data of highway freight volume in Chinese history, the BP neural network prediction model of highway freight volume is established, and the model is coded and calculated in the MATLAB software environment. Through repeated training of the data, the predicted value is finally obtained. The results show that the prediction accuracy of the BP neural network model based on multi-factor prediction is very high. Through the example analysis of China’s highway freight volume, the original data are accurately fitted, and the validity of the highway freight volume prediction model based on BP neural network is proved. Through the prediction of freight volume, the investment in infrastructure construction is improved to promote the development of transportation industry and the progress of social economy.
DTAG: A Methodology for Aggregating Digital Twins Using the WoTDT Ontology
Authors: González-Gerpe Salvador , Poveda-Villalón María , García-Castro Raúl
Published: July 2024

The advancement of digital twins (DTws) has been instrumental in various scientific and industrial domains, facilitating real-time monitoring, analysis, and optimisation of complex systems. However, it remains difficult to describe precisely the architectural models and their characteristics of DTws and the aggregation of lower-level DTws to higher-level DTws. This article introduces two contributions with the goal of addressing challenges in describing DTws architectures and aggregating DTws. Firstly, it presents the development of “WoTDT” (WoT digital twin) ontology, an extension of the W3C Web of Things descriptions ontology, designed to semantically describe the five-dimensional model architecture of DTws. This ontology enhances data interoperability and accessibility across dimensions, promoting a deeper understanding of DTws. Secondly, it introduces the “DTAG” (digital twin aggregation) methodology for aggregating multiple DTws into an unified DTw aggregate (DTwA). This methodology considers whether the DTws contain semantics or not and employs the WoTDT ontology to conceptualise the architecture and features of the resulting DTwA. Finally, an example of WoTDT ontology together with the DTAG methodology is shown in the context of the European H2020 construction-related project COGITO.
Visualization of Isometric Deformations of Helicoidal CMC Surfaces
Authors: Vukojević Filip , Antić Miroslava
Published: July 2024

Helicoidal surfaces of constant mean curvature were fully described by do Carmo and Dajczer. However, the obtained parameterizations are given in terms of somewhat complicated integrals, and as a consequence, not many examples of such surfaces are visualized. In this paper, by using these methods in some particular cases, we provide several interesting visualizations involving these surfaces, mostly as an isometric deformation of a rotational surface. We also give interpretations of some older results involving helicoidal surfaces, motivated by the work carried out by Malkowsky and Veličković. All of the graphics in this paper were created in Wolfram Mathematica.
Development of Printed Pouch Film and Flexible Battery
Authors: Oh Gyeongseok , Mantry Snigdha Paramita , Sim Jae Ho , Cho Hyeon Woo , Won Mijin , Park Hwamok , Park Jiyoung , Lee Juhwan , Kim Dong Soo
Published: July 2024

This study investigates the properties of various adhesives and assesses the effects of the coating and drying conditions of aluminum surface treatment agents on adhesion strength and chemical resistance. The adhesion between aluminum and the polymer film is improved through the application of a surface treatment agent to the aluminum surface. This study examines the initial adhesive strength of a manufactured pouch film with respect to the drying temperature and time and evaluates its adhesive strength in the presence of moisture. The results indicate that the residual moisture on the aluminum surface weakens the adhesive strength and significantly affects electrolyte resistance. A noticeable reduction in strength was observed after water spraying, when the drying temperature and time were relatively low during the initial strength measurement. Among the adhesives used for aluminum and CPP lamination, olefin adhesives exhibit less susceptibility to electrolyte effects and have higher adhesive strengths compared to urethane and ester adhesives. Leveraging these characteristics, flexible pouch cells were manufactured and their stability was evaluated. The results confirm that the flexible cells demonstrate excellent stability, exhibiting potential for application in wearable devices.
Anomaly Detection in Optical Coherence Tomography Angiography (OCTA) with a Vector-Quantized Variational Auto-Encoder (VQ-VAE)
Authors: Jebril Hana , Esengönül Meltem , Bogunović Hrvoje
Published: July 2024

Optical coherence tomography angiography (OCTA) provides detailed information on retinal blood flow and perfusion. Abnormal retinal perfusion indicates possible ocular or systemic disease. We propose a deep learning-based anomaly detection model to identify such anomalies in OCTA. It utilizes two deep learning approaches. First, a representation learning with a Vector-Quantized Variational Auto-Encoder (VQ-VAE) followed by Auto-Regressive (AR) modeling. Second, it exploits epistemic uncertainty estimates from Bayesian U-Net employed to segment the vasculature on OCTA en face images. Evaluation on two large public datasets, DRAC and OCTA-500, demonstrates effective anomaly detection (an AUROC of 0.92 for the DRAC and an AUROC of 0.75 for the OCTA-500) and localization (a mean Dice score of 0.61 for the DRAC) on this challenging task. To our knowledge, this is the first work that addresses anomaly detection in OCTA.
Detection of Cervical Lesion Cell/Clumps Based on Adaptive Feature Extraction
Authors: Li Gang , Li Xingguang , Wang Yuting , Gong Shu , Yang Yanting , Xu Chuanyun
Published: July 2024

Automated detection of cervical lesion cell/clumps in cervical cytological images is essential for computer-aided diagnosis. In this task, the shape and size of the lesion cell/clumps appeared to vary considerably, reducing the detection performance of cervical lesion cell/clumps. To address the issue, we propose an adaptive feature extraction network for cervical lesion cell/clumps detection, called AFE-Net. Specifically, we propose the adaptive module to acquire the features of cervical lesion cell/clumps, while introducing the global bias mechanism to acquire the global average information, aiming at combining the adaptive features with the global information to improve the representation of the target features in the model, and thus enhance the detection performance of the model. Furthermore, we analyze the results of the popular bounding box loss on the model and propose the new bounding box loss tendency-IoU (TIoU). Finally, the network achieves the mean Average Precision (mAP) of 64.8% on the CDetector dataset, with 30.7 million parameters. Compared with YOLOv7 of 62.6% and 34.8M, the model improved mAP by 2.2% and reduced the number of parameters by 11.8%.
In Vitro Bioassay for Damage-Associated Molecular Patterns Arising from Injured Oral Cells
Authors: Panahipour Layla , Micucci Chiara , Gelmetti Benedetta , Gruber Reinhard
Published: July 2024

Gingival fibroblasts are a significant source of paracrine signals required to maintain periodontal homeostasis and to mediate pathological events linked to periodontitis and oral squamous cell carcinomas. Among the potential paracrine signals are stanniocalcin-1 (STC1), involved in oxidative stress and cellular survival; amphiregulin (AREG), a growth factor that mediates the cross-talk between immune cells and epithelial cells; chromosome 11 open reading frame 96 (C11orf96) with an unclear biologic function; and the inflammation-associated prostaglandin E synthase (PTGES). Gingival fibroblasts increasingly express these genes in response to bone allografts containing remnants of injured cells. Thus, the gene expression might be caused by the local release of damage-associated molecular patterns arising from injured cells. The aim of this study is consequently to use the established gene panel as a bioassay to measure the damage-associated activity of oral cell lysates. To this aim, we have exposed gingival fibroblasts to lysates prepared from the squamous carcinoma cell lines TR146 and HSC2, oral epithelial cells, and gingival fibroblasts. We report here that all lysates significantly increased the transcription of the entire gene panel, supported for STC1 at the protein level. Blocking TGF-β receptor 1 kinase with SB431542 only partially reduced the forced expression of STC1, AREG, and C11orf96. SB431542 even increased the PTGES expression. Together, these findings suggest that the damage signals originating from oral cells can change the paracrine activity of gingival fibroblasts. Moreover, the expression panel of genes can serve as a bioassay for testing the biocompatibility of materials for oral application.
Post-Translational Modifications (PTMs) of mutp53 and Epigenetic Changes Induced by mutp53
Authors: Benedetti Rossella , Di Crosta Michele , D’Orazi Gabriella , Cirone Mara
Published: July 2024

Wild-type (wt) p53 and mutant forms (mutp53) play a key but opposite role in carcinogenesis. wtP53 acts as an oncosuppressor, preventing oncogenic transformation, while mutp53, which loses this property, may instead favor this process. This suggests that a better understanding of the mechanisms activating wtp53 while inhibiting mutp53 may help to design more effective anti-cancer treatments. In this review, we examine possible PTMs with which both wt- and mutp53 can be decorated and discuss how their manipulation could represent a possible strategy to control the stability and function of these proteins, focusing in particular on mutp53. The impact of ubiquitination, phosphorylation, acetylation, and methylation of p53, in the context of several solid and hematologic cancers, will be discussed. Finally, we will describe some of the recent studies reporting that wt- and mutp53 may influence the expression and activity of enzymes responsible for epigenetic changes such as acetylation, methylation, and microRNA regulation and the possible consequences of such changes.
Early Life Adversity, Microbiome, and Inflammatory Responses
Authors: Beurel Eléonore , Nemeroff Charles B.
Published: July 2024

Early life adversity has a profound impact on physical and mental health. Because the central nervous and immune systems are not fully mature at birth and continue to mature during the postnatal period, a bidirectional interaction between the central nervous system and the immune system has been hypothesized, with traumatic stressors during childhood being pivotal in priming individuals for later adult psychopathology. Similarly, the microbiome, which regulates both neurodevelopment and immune function, also matures during childhood, rendering this interaction between the brain and the immune system even more complex. In this review, we provide evidence for the role of the immune response and the microbiome in the deleterious effects of early life adversity, both in humans and rodent models.
Amino-Acid Characteristics in Protein Native State Structures
Authors: Škrbić Tatjana , Giacometti Achille , Hoang Trinh X. , Maritan Amos , Banavar Jayanth R.
Published: July 2024

The molecular machines of life, proteins, are made up of twenty kinds of amino acids, each with distinctive side chains. We present a geometrical analysis of the protrusion statistics of side chains in more than 4000 high-resolution protein structures. We employ a coarse-grained representation of the protein backbone viewed as a linear chain of Cα atoms and consider just the heavy atoms of the side chains. We study the large variety of behaviors of the amino acids based on both rudimentary structural chemistry as well as geometry. Our geometrical analysis uses a backbone Frenet coordinate system for the common study of all amino acids. Our analysis underscores the richness of the repertoire of amino acids that is available to nature to design protein sequences that fit within the putative native state folds.
Role of Specificity Protein 1 (SP1) in Cardiovascular Diseases: Pathological Mechanisms and Therapeutic Potentials
Authors: Ding Jie , Fayyaz Aminah I. , Ding Yuchuan , Liang Dandan , Luo Ming
Published: July 2024

In mammals, specificity protein 1 (SP1) was the first Cys2-His2 zinc finger transcription factor to be isolated within the specificity protein and Krüppel-like factor (Sp/KLF) gene family. SP1 regulates gene expression by binding to Guanine–Cytosine (GC)-rich sequences on promoter regions of target genes, affecting various cellular processes. Additionally, the activity of SP1 is markedly influenced by posttranslational modifications, such as phosphorylation, acetylation, glycosylation, and proteolysis. SP1 is implicated in the regulation of apoptosis, cell hypertrophy, inflammation, oxidative stress, lipid metabolism, plaque stabilization, endothelial dysfunction, fibrosis, calcification, and other pathological processes. These processes impact the onset and progression of numerous cardiovascular disorders, including coronary heart disease, ischemia-reperfusion injury, cardiomyopathy, arrhythmia, and vascular disease. SP1 emerges as a potential target for the prevention and therapeutic intervention of cardiac ailments. In this review, we delve into the biological functions, pathophysiological mechanisms, and potential clinical implications of SP1 in cardiac pathology to offer valuable insights into the regulatory functions of SP1 in heart diseases and unveil novel avenues for the prevention and treatment of cardiovascular conditions.
West Nile Virus-Induced Expression of Senescent Gene Lgals3bp Regulates Microglial Phenotype within Cerebral Cortex
Authors: Arutyunov Artem , Durán-Laforet Violeta , Ai Shenjian , Ferrari Loris , Murphy Robert , Schafer Dorothy P. , Klein Robyn S.
Published: July 2024

Microglia, the resident macrophages of the central nervous system, exhibit altered gene expression in response to various neurological conditions. This study investigates the relationship between West Nile Virus infection and microglial senescence, focusing on the role of LGALS3BP, a protein implicated in both antiviral responses and aging. Using spatial transcriptomics, RNA sequencing and flow cytometry, we characterized changes in microglial gene signatures in adult and aged mice following recovery from WNV encephalitis. Additionally, we analyzed Lgals3bp expression and generated Lgals3bp-deficient mice to assess the impact on neuroinflammation and microglial phenotypes. Our results show that WNV-activated microglia share transcriptional signatures with aged microglia, including upregulation of genes involved in interferon response and inflammation. Lgals3bp was broadly expressed in the CNS and robustly upregulated during WNV infection and aging. Lgals3bp-deficient mice exhibited reduced neuroinflammation, increased homeostatic microglial numbers, and altered T cell populations without differences in virologic control or survival. These data indicate that LGALS3BP has a role in regulating neuroinflammation and microglial activation and suggest that targeting LGALS3BP might provide a potential route for mitigating neuroinflammation-related cognitive decline in aging and post-viral infections.
Design, Synthesis, Characterization, and Cytotoxicity of New Pyrazolylmethylene-2-thioxoimidazolidin-4-one Derivatives towards Androgen-Sensitive LNCaP Prostate Cancer Cells
Authors: El-Atawy Mohamed A. , Kebeish Rashad , Almotairy Awatif Rashed Z. , Omar Alaa Z.
Published: July 2024

A new class of pyrazolylmethylene-2-thioxoimidazolidin-4-one derivatives 3a–p were rationally designed and synthesized with the aim of exploring their potential as treatments for prostate cancer. The synthesized compounds 3a–p were biologically analyzed for their anticancer effects against AR+LNCaP, AR-PC-3, and Wi38 cell lines. The observed IC50 values against AR+LNCaP ranged between 10.27 ± 0.14 and 109.72 ± 2.06 µM after 24 h of incubation. Compounds 3i–k, 3m, and 3o–p recorded IC50 values of 05.22 ± 0.12 to 11.75 ± 0.07 µM after 48 h incubation in the presence of 1 nM DHT, with higher selectivity towards AR+LNCaP. Moreover, compounds 3i and 3k significantly induced Caspase 3 accumulation, reduced DNA content at the various stages of the cell cycle, and ultimately caused AR+LNCaP cell growth arrest, as confirmed by cell apoptosis assays. These findings suggest that these analogues of androgen receptor blockers have promising potential for further investigation as effective treatments for prostate cancer.
Precision Medicine in Inflammatory Bowel Disease: A Spotlight on Emerging Molecular Biomarkers
Authors: Mestrovic Antonio , Perkovic Nikola , Bozic Dorotea , Kumric Marko , Vilovic Marino , Bozic Josko
Published: July 2024

Inflammatory bowel diseases (IBD) remain challenging in terms of understanding their causes and in terms of diagnosing, treating, and monitoring patients. Modern diagnosis combines biomarkers, imaging, and endoscopic methods. Common biomarkers like CRP and fecal calprotectin, while invaluable tools, have limitations and are not entirely specific to IBD. The limitations of existing markers and the invasiveness of endoscopic procedures highlight the need to discover and implement new markers. With an ideal biomarker, we could predict the risk of disease development, as well as the possibility of response to a particular therapy, which would be significant in elucidating the pathogenesis of the disease. Recent research in the fields of machine learning, proteomics, epigenetics, and gut microbiota provides further insight into the pathogenesis of the disease and is also revealing new biomarkers. New markers, such as BAFF, PGE-MUM, oncostatin M, microRNA panels, αvβ6 antibody, and S100A12 from stool, are increasingly being identified, with αvβ6 antibody and oncostatin M being potentially close to being presented into clinical practice. However, the specificity of certain markers still remains problematic. Furthermore, the use of expensive and less accessible technology for detecting new markers, such as microRNAs, represents a limitation for widespread use in clinical practice. Nevertheless, the need for non-invasive, comprehensive markers is becoming increasingly important regarding the complexity of treatment and overall management of IBD.
Force Control of a Haptic Flexible-Link Antenna Based on a Lumped-Mass Model
Authors: Haro-Olmo María Isabel , Mérida-Calvo Luis , Feliu-Talegón Daniel , Feliu-Batlle Vicente
Published: July 2024

Haptic organs are common in nature and help animals to navigate environments where vision is not possible. Insects often use slender, lightweight, and flexible links as sensing antennae. These antennae have a muscle-endowed base that changes their orientation and an organ that senses the applied force and moment, enabling active sensing. Sensing antennae detect obstacles through contact during motion and even recognize objects. They can also push obstacles. In all these tasks, force control of the antenna is crucial. The objective of our research is to develop a haptic robotic system based on a sensing antenna, consisting of a very lightweight and slender flexible rod. In this context, the work presented here focuses on the force control of this device. To achieve this, (a) we develop a dynamic model of the antenna that moves under gravity and maintains point contact with an object, based on lumped-mass discretization of the rod; (b) we prove the robust stability property of the closed-loop system using the Routh stability criterion; and (c) based on this property, we design a robust force control system that performs efficiently regardless of the contact point with the object. We built a mechanical device replicating this sensing organ. It is a flexible link connected at one end to a 3D force–torque sensor, which is attached to a mechanical structure with two DC motors, providing azimuthal and elevation movements to the antenna. Our experiments in contact situations demonstrate the effectiveness of our control method.
Laser Ablating Biomimetic Periodic Array Fish Scale Surface for Drag Reduction
Authors: Chen Dengke , Zhang Bowen , Zhang Haifeng , Shangguan Zheng , Sun Chenggang , Cui Xianxian , Liu Xiaolin , Zhao Zehui , Liu Guang , Chen Huawei
Published: July 2024

Reducing resistance to surface friction is challenging in the field of engineering. Natural biological systems have evolved unique functional surfaces or special physiological functions to adapt to their complex environments over centuries. Among these biological wonders, fish, one of the oldest in the vertebrate group, have garnered attention due to their exceptional fluid dynamics capabilities. Fish skin has inspired innovation in reducing surface friction due to its unique structures and material properties. Herein, drawing inspiration from the unique properties of fish scales, a periodic array of fish scales was fabricated by laser ablation on a polished aluminum template. The morphology of the biomimetic fish scale surface was characterized using scanning electron microscopy and a white-light interfering profilometer. Drag reduction performance was measured in a closed circulating water tunnel. The maximum drag reduction was 10.26% at a Reynolds number of 39,532, and the drag reduction performance gradually decreased with an increase in the distance between fish scales. The mechanism of the biomimetic drag reduction surface was analyzed using computational fluid dynamics. Streamwise vortices were generated at the valley of the biomimetic fish scale, replacing sliding friction with rolling friction. These results are expected to provide a foundation for in-depth analysis of the hydrodynamic performance of fish and serve as new inspiration for drag reduction and antifouling.
Neural Simulation of Actions for Serpentine Robots
Authors: Morasso Pietro
Published: July 2024

The neural or mental simulation of actions is a powerful tool for allowing cognitive agents to develop Prospection Capabilities that are crucial for learning and memorizing key aspects of challenging skills. In previous studies, we developed an approach based on the animation of the redundant human body schema, based on the Passive Motion Paradigm (PMP). In this paper, we show that this approach can be easily extended to hyper-redundant serpentine robots as well as to hybrid configurations where the serpentine robot is functionally integrated with a traditional skeletal infrastructure. A simulation model is analyzed in detail, showing that it incorporates spatio-temporal features discovered in the biomechanical studies of biological hydrostats, such as the elephant trunk or octopus tentacles. It is proposed that such a generative internal model could be the basis for a cognitive architecture appropriate for serpentine robots, independent of the underlying design and control technologies. Although robotic hydrostats have received a lot of attention in recent decades, the great majority of research activities have been focused on the actuation/sensorial/material technologies that can support the design of hyper-redundant soft/serpentine robots, as well as the related control methodologies. The cognitive level of analysis has been limited to motion planning, without addressing synergy formation and mental time travel. This is what this paper is focused on.
An Improved Football Team Training Algorithm for Global Optimization
Authors: Hou Jun , Cui Yuemei , Rong Ming , Jin Bo
Published: July 2024

The football team training algorithm (FTTA) is a new metaheuristic algorithm that was proposed in 2024. The FTTA has better performance but faces challenges such as poor convergence accuracy and ease of falling into local optimality due to limitations such as referring too much to the optimal individual for updating and insufficient perturbation of the optimal agent. To address these concerns, this paper presents an improved football team training algorithm called IFTTA. To enhance the exploration ability in the collective training phase, this paper proposes the fitness distance-balanced collective training strategy. This enables the players to train more rationally in the collective training phase and balances the exploration and exploitation capabilities of the algorithm. To further perturb the optimal agent in FTTA, a non-monopoly extra training strategy is designed to enhance the ability to get rid of the local optimum. In addition, a population restart strategy is then designed to boost the convergence accuracy and population diversity of the algorithm. In this paper, we validate the performance of IFTTA and FTTA as well as six comparison algorithms in CEC2017 test suites. The experimental results show that IFTTA has strong optimization performance. Moreover, several engineering-constrained optimization problems confirm the potential of IFTTA to solve real-world optimization problems.
Factor Structures in the Depressive Symptoms Domains in the 9Q for Northern Thai Adults and Their Association with Chronic Diseases
Authors: Kawilapat Suttipong , Traisathit Patrinee , Maneeton Narong , Prasitwattanaseree Sukon , Kongsuk Thoranin , Arunpongpaisal Suwanna , Leejongpermpoon Jintana , Sukhawaha Supattra , Maneeton Benchalak
Published: July 2024

Most of the common models to examine depression are one-factor models; however, previous studies provided several-factor structure models on each depressive symptom using the Patient Health Questionnaire-9 (PHQ-9). The Nine-Questions Depression-Rating Scale (9Q) is an alternative assessment tool that was developed for assessing the severity of depressive symptoms in Thai adults. This study aimed to examine the factor structure of this tool based on the factor structure models for the PHQ-9 provided in previous studies using confirmatory factor analysis (CFA). We also examined the association of chronic diseases and depressive symptoms using the Multiple Indicators Multiple Causes model among 1346 participants aged 19 years old or more without psychiatric disorders. The results show that the two-factor CFA model with six items in the cognitive-affective domain and three items in the somatic domain provided the best fit for depressive symptoms in the study population (RMSEA = 0.077, CFI = 0.953, TLI = 0.936). Dyslipidemia was positively associated with both cognitive-affective symptoms (β = 0.120) and somatic depressive symptoms (β = 0.080). Allergies were associated with a higher level of cognitive-affective depressive symptoms (β = 0.087), while migraine (β = 0.114) and peptic ulcer disease (β = 0.062) were associated with a higher level of somatic symptoms. Increased age was associated with a lower level of somatic symptoms (β = −0.088). Our findings suggested that considering depressive symptoms as two dimensions yields a better fit for depressive symptoms. The co-occurrence of chronic diseases associated with depressive symptoms should be monitored.