bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the Tumor-Associated Risk of Mesenchymal Stem Cell Therapy in Veterinary Medicine
Authors: Jeung Soyoung , Kim Sungsoo , Ah Jaegon , Seo Sanghyuk , Jan Umair , Lee Hyejin , Lee Jeong Ik
Published: March 2024

Mesenchymal stem cell (MSC) therapy has been actively applied in veterinary regenerative medicine to treat various canine and feline diseases. With increasing emphasis on safe cell-based therapies, evaluations of their tumorigenic potential are in great demand. However, a direct confirmation of whether tumors originate from stem cells or host cells is not easily achievable. Additionally, previous studies evaluating injections of high doses of MSCs into nude mice did not demonstrate tumor formation. Recent research focused on optimizing MSC-based therapies for veterinary patients, such as MSC-derived extracellular vesicles in treating different diseases. This progress also signifies a broader shift towards personalized veterinary medicine, where treatments can be tailored to individual pets based on their unique genetic profiles. These findings related to different treatments using MSCs emphasize their future potential for veterinary clinical applications. In summary, because of lower tumor-associated risk of MSCs as compared to embryonic and induced pluripotent stem cells, MSCs are considered a suitable source for treating various canine and feline diseases.
Feline Forensics: Revealing the Unique Decomposition of Cats
Authors: Bagsby Kelly , Stern Adam W. , Hans Krystal R.
Published: March 2024

Limited data about the differences between the decomposition of animals with fur exist within the forensic veterinary medicine discipline. Due to the sheer number of animals used as animal models and the number of animals that exist, determining stages of decomposition that are applicable for all species is difficult. Typically, assessing what stage of decomposition a decedent is in is subjective due to the visual assessment of decomposition changes. A method developed to rectify this issue was the total body score (TBS) method, which assigns a numerical value based on the degree of decomposition to the head, torso, and limbs. The purpose of this study was to examine the decomposition of cats, identify any unique decomposition characteristics, and determine the rate of decomposition using total body score and accumulated degree days (ADDs). Twelve domestic short-haired cats were placed in a grassy field in West Lafayette, IN. An assessment of TBS was documented for each cat and each experimental group. An exponential relationship between TBS and ADD was documented. Overall, there was not a significant difference in the rate of decomposition or total body scores between the groups (Z = −91.00, p = 0.0672).
Can the Inclusion of Forage Chicory in the Diet of Lactating Dairy Cattle Alter Milk Production and Milk Fatty Acid Composition? Findings of a Multilevel Meta-Analysis
Authors: Mangwe Mancoba , Bryant Racheal , Olszewski Antonia , Herath Hitihamy , Al-Marashdeh Omar
Published: March 2024

In traditional ryegrass/white clover (Lolium perenne L./Trifolium repens L.) pastoral systems, forage herbs such as chicory (Cichorium intybus L.) present an opportunity to fill feed deficits during late spring and summer. Although multiple research publications have evaluated the efficacy of chicory for enhancing milk production and milk fatty acid (FA) profile, no publication has quantitatively synthesised the body of research. This systematic review and meta-analysis examined the effect of chicory on milk production and composition, as well as on the milk fatty acid composition of dairy cattle. A total of 29 comparisons from 15 unique research publications involving 597 dairy cattle were used to develop a dataset for analysis. Three-level random-effect and robust variance estimator models were used to account for the hierarchical structure of the data and the dependency of effect sizes within publications. Chicory inclusion increased milk yield when compared to grass-based diets {weighted mean difference (WMD) = 1.07 (95% CI 0.54–1.60) kg/cow/d, p < 0.001}, but it provided a similar milk yield when compared to other forages such as legumes and herbs {dicots; WMD = −0.30, (95% CI −89–0.29) kg/cow/day, p = 0.312}. Increases in milk yield were congruent with differences in DM intake (p = 0.09) and ME intakes (p = 0.003), being similar in chicory-fed and dicot-fed cows but higher than grass-fed cows. Chicory feeding’s effect on milk solids was twice as high during mid lactation {154 days in milk; WMD = 0.13, (95% 0.081–0.175) kg/cow/day, p < 0.001} as during late lactation {219 days in milk; WMD = 0.06, (95% 0.003–0.13) kg/cow/day, p = 0.041}. In line with milk yield, greater and more significant effect sizes were found for alpha linolenic acid {ALA; WMD = 0.20 (95% CI 0.06–0.35) g/100 g FA, p = 0.011} when chicory was compared to grass species only. Comparing chicory with dicots suggests that chicory inclusion did not impact ALA concentrations {WMD = 0.001 (95% CI −0.02–0.2) g/100 g FA, p = 0.99}. There were no differences in conjugated linoleic acid concentration in the milk of cows fed chicory or control diets. The study provides empirical evidence of chicory’s efficacy for improved milk production and milk fatty acid composition.
Systemic Oxidative Balance Reflects the Liver Disease Progression Status for Primary Biliary Cholangitis (Pbc): The Narcissus Fountain
Authors: Dallio Marcello , Romeo Mario , Cipullo Marina , Ventriglia Lorenzo , Scognamiglio Flavia , Vaia Paolo , Iadanza Giorgia , Coppola Annachiara , Federico Alessandro
Published: March 2024

Biological antioxidant potential (BAP) and Reactive Oxygen Metabolites (dROMs) are two tests complementarily assessing systemic oxidative statuses (SOSs) that are never applied in chronic liver disorders (CLDs). We enrolled 41 ursodeoxycholic acid (UDCA)-naïve Primary Biliary Cholangitis (PBC) patients [age: 58.61 ± 11.26 years; females (F): 39], 40 patients with metabolic-dysfunction-associated steatotic livers (age: 54.30 ± 11.21; F: 20), 52 patients with HBV (age: 52.40 ± 8.22; F: 34), 50 patients with (age: 56.44 ± 7.79, F: 29), and 10 controls (age: 52.50 ± 9.64; F: 7). Liver fibrosis and the steatosis severity were determined using transient elastography, and the SOS was balanced using d-ROMs and the BAP test. The gene expressions of superoxide dismutase (SOD1; SOD2) and glutathione peroxidase (GPx1) were evaluated using real-time PCR in advanced fibrosis (AF: F3F4) in patients with PBC. In contrast to other CLDs, in PBC the dROMs and BAP levels were, respectively, directly and inversely correlated with hepatic fibrosis (dROMs, R: 0.883; BAP, R: −0.882) and steatosis (dROMs, R: 0.954; BAP, R: −0931) severity (p < 0.0001 all). Patients with PBC also revealed a progressively increasing trend of d-ROMs (F0–F2 vs. F3: p = 0.0008; F3 vs. F4: p = 0.04) and reduction in BAP levels (F0–F2 vs. F3: p = 0.0007; F3 vs. F4 p = 0.04) according to the worsening of liver fibrosis. In AF-PBC, the SOD1, SOD2, and GPx1 expressions were significantly downregulated in patients presenting SOS imbalance (SOD1, p = 0.02; SOD2, p = 0.03; GPx1, p = 0.02). SOS disequilibrium represents a leitmotiv in patients with PBC, perfectly reflecting their liver disease progression status.
Investigating the Neuroprotective and Cognitive-Enhancing Effects of Bacopa monnieri: A Systematic Review Focused on Inflammation, Oxidative Stress, Mitochondrial Dysfunction, and Apoptosis
Authors: Valotto Neto Luiz José , Reverete de Araujo Matheus , Moretti Junior Renato Cesar , Mendes Machado Nathalia , Joshi Rakesh Kumar , dos Santos Buglio Daiene , Barbalho Lamas Caroline , Direito Rosa , Fornari Laurindo Lucas , Tanaka Masaru , Barbalho Sandra Maria
Published: March 2024

The aging of the global population has increased the prevalence of neurodegenerative conditions. Bacopa monnieri (BM), an herb with active compounds, such as bacosides A and B, betulinic acid, loliolide, asiatic acid, and quercetin, demonstrates the potential for brain health. Limited research has been conducted on the therapeutic applications of BM in neurodegenerative conditions. This systematic review aims to project BM’s beneficial role in brain disorders. BM has anti-apoptotic and antioxidant actions and can repair damaged neurons, stimulate kinase activity, restore synaptic function, improve nerve transmission, and increase neuroprotection. The included twenty-two clinical trials demonstrated that BM can reduce Nuclear Factor-κB phosphorylation, improve emotional function, cognitive functions, anhedonia, hyperactivity, sleep routine, depression, attention deficit, learning problems, memory retention, impulsivity, and psychiatric problems. Moreover, BM can reduce the levels of pro-inflammatory biomarkers and oxidative stress. Here, we highlight that BM provides notable therapeutic benefits and can serve as a complementary approach for the care of patients with neurodegenerative conditions associated with brain disorders. This review adds to the growing interest in natural products and their potential therapeutic applications by improving our understanding of the mechanisms underlying cognitive function and neurodegeneration and informing the development of new therapeutic strategies for neurodegenerative diseases.
Vehicle Multi-Object Detection and Tracking Algorithm Based on Improved You Only Look Once 5s Version and DeepSORT
Authors: Bui Thioanh , Wang Guihao , Wei Geng , Zeng Qian
Published: March 2024

The increasing popularity of vehicles has led to traffic congestion and frequent traffic accidents. Intelligent transportation technology is an effective solution to this problem. In order to improve the accuracy and effectiveness of vehicle detection and tracking, this paper combined the improved YOLOv5s model with the optimized DeepSORT tracking algorithm to detect and track vehicles on traffic roads. Firstly, in the detection model of YOLOv5s, the Attention-based Intra-scale Feature Interaction (AIFI) module is introduced to detect vehicles more quickly and accurately. Secondly, the Kalman filtering (KF) algorithm of DeepSORT is optimized to improve the accuracy of predictions of the vehicle state by using the width to replace the length-to-width ratio of the vehicle prediction box in the original KF algorithm. Finally, in the re-recognition network of DeepSORT, the original Convolutional Neural Network (CNN) model is replaced by an improved ResNet36 as the backbone network for feature extraction. The experimental results show that, compared with the original algorithm, when examining the performance of the improved algorithm in terms of target detection, the recall rate, average accuracy (mAP), and detection speed, are increased by 7.7%, 15.5%, and 14.2%, respectively; in terms of multi-object tracking performance, such as multi-object tracking precision (MOTP) and multi-object tracking accuracy (MOTA), improvements of 14.84% and 9.62%, respectively, are obtained and the total number of times a trajectory is fragmented (Frag) is reduced by 32.52%.These results indicate that the proposed algorithm can meet the requirements of accuracy, real-time detection, and stable vehicle detection and tracking on traffic roads.
Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian
Authors: Nitu Melania , Dascalu Mihai
Published: March 2024

Authorship attribution for less-resourced languages like Romanian, characterized by the scarcity of large, annotated datasets and the limited number of available NLP tools, poses unique challenges. This study focuses on a hybrid Transformer combining handcrafted linguistic features, ranging from surface indices like word frequencies to syntax, semantics, and discourse markers, with contextualized embeddings from a Romanian BERT encoder. The methodology involves extracting contextualized representations from a pre-trained Romanian BERT model and concatenating them with linguistic features, selected using the Kruskal–Wallis mean rank, to create a hybrid input vector for a classification layer. We compare this approach with a baseline ensemble of seven machine learning classifiers for authorship attribution employing majority soft voting. We conduct studies on both long texts (full texts) and short texts (paragraphs), with 19 authors and a subset of 10. Our hybrid Transformer outperforms existing methods, achieving an F1 score of 0.87 on the full dataset of the 19-author set (an 11% enhancement) and an F1 score of 0.95 on the 10-author subset (an increase of 10% over previous research studies). We conduct linguistic analysis leveraging textual complexity indices and employ McNemar and Cochran’s Q statistical tests to evaluate the performance evolution across the best three models, while highlighting patterns in misclassifications. Our research contributes to diversifying methodologies for effective authorship attribution in resource-constrained linguistic environments. Furthermore, we publicly release the full dataset and the codebase associated with this study to encourage further exploration and development in this field.
Highway Microgrid Project Evaluation under Energy Transportation Integration
Authors: Yao Lei , Fu Hao , Shao Ming , Yu Zuyao
Published: March 2024

The construction of highway microgrids is evolving into a new highway energy system that integrates “Source-Network-Load-Storage”. This paper provides a comprehensive evaluation of expressway microgrids from the perspective of transportation and energy integration. An index model is set up that considers the economy, technology, and environment. The grey evaluation method, on the strength of analytic hierarchy process–entropy weight method, shows that the integrated microgrid of “source-network-load-storage” promotes energy sustainability, supply reliability, and sustainable environmental development. When compared to different evaluation methods for microgrid planning schemes, our method yields the highest score of 0.9127, indicating superior results. This approach is suitable for the multicomponent evaluation system of expressway microgrids and allows for scientifically evaluating microgrid planning schemes.
CourseKG: An Educational Knowledge Graph Based on Course Information for Precision Teaching
Authors: Li Ying , Liang Yu , Yang Runze , Qiu Jincheng , Zhang Chenlong , Zhang Xiantao
Published: March 2024

With the rapid development of advanced technologies, such as artificial intelligence and deep learning, educational informatization has entered a new era. However, the explosion of information has brought numerous challenges. Knowledge graphs, as a crucial component of artificial intelligence, can contribute to the quality of teaching. This study proposes an educational knowledge graph based on course information named CourseKG for precision teaching. Precision teaching seeks to individualize the curriculum for each learner and optimize learning efficiency. CourseKG aims to establish a correct and comprehensive curriculum knowledge system and promote personalized learning paths. CourseKG can address the issue that current general-purpose knowledge graphs are not suitable for the education field. Particularly, this study proposes a framework for educational entity recognition based on the pre-trained BERT model. This framework captures relevant information in the educational domain using the BERT model and combines it with the BiGRU and multi-head self-attention mechanism to extract multi-scale and multi-level global dependency relationships. In addition, the CRF is used for character-label decoding. Further, a relationship extraction method based on the BERT model, which integrates sentence features and educational entities and estimates the similarity between knowledge pairs using cosine similarity, is proposed. The proposed CourseKG is verified by experiments using real-world C programming course data. The experimental results demonstrate the effectiveness of CourseKG. Finally, the results show that the proposed CourseKG can significantly enhance the precision teaching quality and realize multi-directional adaptation among teachers, courses, and students.
Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
Authors: Kim Gun-Woo , Ju Chan-Yang , Seok Hyeri , Lee Dong-Ho
Published: March 2024

During outbreaks of infectious diseases, such as COVID-19, it is critical to rapidly determine treatment priorities and identify patients requiring hospitalization based on clinical severity. Although various machine learning models have been developed to predict COVID-19 severity, most have limitations, such as small dataset sizes, the limited availability of clinical variables, or a constrained classification of severity levels by a single classifier. In this paper, we propose an adaptive stacking ensemble technique that identifies various COVID-19 patient severity levels and separates them into three formats: Type 1 (low or high severity), Type 2 (mild, severe, critical), and Type 3 (asymptomatic, mild, moderate, severe, fatal). To enhance the model’s generalizability, we utilized a nationwide dataset from the South Korean government, comprising data from 5644 patients across over 100 hospitals. To address the limited availability of clinical variables, our technique employs data-driven strategies and a proposed feature selection method. This ensures the availability of clinical variables across diverse hospital environments. To construct optimal stacking ensemble models, our technique adaptively selects candidate base classifiers by analyzing the correlation between their predicted outcomes and performance. It then automatically determines the optimal multi-layer combination of base and meta-classifiers using a greedy search algorithm. To further improve the performance, we applied various techniques, including imputation of missing values and oversampling. The experimental results demonstrate that our stacking ensemble models significantly outperform existing single classifiers and AutoML approaches, with improvements of 6.42% and 8.86% in F1 and AUC scores for Type 1, 9.59% and 6.68% for Type 2, and 11.94% and 9.24% for Type 3, respectively. Consequently, our approach improves the prediction of COVID-19 severity levels and potentially assists frontline healthcare providers in making informed decisions.
Prediction of Soil Compaction Parameters Using Machine Learning Models
Authors: Li Bingyi , You Zixuan , Ni Kaiwei , Wang Yuexiang
Published: March 2024

Maximum Dry Density (MDD) and Optimum Moisture Content (OMC) are two important parameters of soil filling, which affect the soil stability and bearing capacity, and thus the reliability and durability of facilities such as highways and bridges. Therefore, it is important to make reasonable predictions of OMC and MDD. Four machine learning algorithms, namely, Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and Extreme Gradient Boosting Tree (XGBoost), are adopted in this paper to establish MDD and OMC prediction models. After training and testing, the best models of the four algorithms are compared. The results show that, as an ensemble learning algorithm, XGBoost is the best model for predicting MDD and OMC, with an R2 of 0.9234 for OMC, and an R2 of 0.9098 for MDD. Finally, the feature importance analysis concludes that the plastic limit (PL) and the liquid limit (LL) are the two features that affect OMC and MDD the most. The prediction of soil compaction parameters using machine learning models, especially ensemble learning, can significantly reduce the amount of laboratory work and improve the efficiency of optimizing design for soil resource utilization in engineering construction.
A Four-Port Dual-Band Dual-Polarized Antenna for Ku-Band Satellite Communications
Authors: Trinh-Van Son , Yang Woo Yong , Cho Hyung Won , Hwang Keum Cheol
Published: March 2024

This paper presents the development of a four-port dual-band dual-polarized antenna for transmitting/receiving (Tx/Rx) applications in Ku-band satellite communications. The antenna consists of two antenna elements sharing a common radiating aperture, a low-band element formed by an L-probe proximity-fed square-ring radiator for operation at the Rx band of 10.7–12.75 GHz, and a high-band element realized by a stacked-patch radiator for operation at the Tx band of 13.75–14.5 GHz. Within a compact multilayer structure, the antenna achieves wide dual-band operation, with each band having the ability to simultaneously operate with dual polarization. An antenna prototype is fabricated and tested to verify its performance. The experimental results show that the proposed antenna achieves an impedance bandwidth of 9.28–12.96 GHz (13.21–15.32 GHz), an isolation value between two orthogonal polarizations of 12 dB (12.4 dB), and a peak gain of 6.63 dBi (5.42 diBi) at the low band (high band). Tx/Rx isolation of more than 14 dB is achieved in both the Rx band and Tx band.
Graphene-Enhanced CuW Composites for High-Voltage Circuit Breaker Electrical Contacts
Authors: Liu Tan , Han Yu , Jia Dongchen , Pang Zhen , Fu Yuwei , Song Zhongxiao , Ding Yi
Published: March 2024

To address the issue of over-standard short-circuit currents in a power system, it is imperative to enhance the comprehensive performance of the electrical contacts, which serve as the lynchpin of circuit breakers, so as to improve the breaking capacity of high-voltage circuit breakers. Graphene, as the most prominent two-dimensional carbon material in recent years, has garnered widespread applications across various fields. In this study, graphene-enhanced CuW composites for high-voltage circuit breaker electrical contacts were prepared innovatively using integrated vacuum infiltration technology. The innovative graphene-enhanced CuW composites significantly improved the mechanical, electrical, and ablation resistance properties, and have been successfully applied in the 252 kV/63 kA high-voltage SF6 circuit breakers, achieving 20 times effective consecutive full-capacity short-circuit current breaking. It provides a new route for the development and application of high-performance CuW electrical contacts. Looking ahead, it is planned to extend their application to higher voltage grade high-voltage circuit breakers.
Is the Artificial Pollination of Walnut Trees with Drones Able to Minimize the Presence of Xanthomonas arboricola pv. juglandis? A Review
Authors: Manthos Ioannis , Sotiropoulos Thomas , Vagelas Ioannis
Published: March 2024

Walnut (Juglans regia L.) is a monoecious species and although it exhibits self-compatibility, it presents incomplete overlap of pollen shed and female receptivity. Thus, cross-pollination is prerequisite for optimal fruit production. Cross-pollination can occur naturally by wind, insects, artificially, or by hand. Pollen has been recognized as one possible pathway for Xanthomonas arboricola pv. juglandis infection, a pathogenic bacterium responsible for walnut blight disease. Other than the well-known cultural and chemical control practices, artificial pollination technologies with the use of drones could be a successful tool for walnut blight disease management in orchards. Drones may carry pollen and release it over crops or mimic the actions of bees and other pollinators. Although this new pollination technology could be regarded as a promising tool, pollen germination and knowledge of pollen as a potential pathway for the dissemination of bacterial diseases remain crucial information for the development and production of aerial pollinator robots for walnut trees. Thus, our purpose was to describe a pollination model with fundamental components, including the identification of the “core” pollen microbiota, the use of drones for artificial pollination as a successful tool for managing walnut blight disease, specifying an appropriate flower pollination algorithm, design of an autonomous precision pollination robot, and minimizing the average errors of flower pollination algorithm parameters through machine learning and meta-heuristic algorithms.
Acute Effect of Velocity-Based Resistance Training on Subsequent Endurance Running Performance: Volume and Intensity Relevance
Authors: Pérez-Castilla Alejandro , Ruiz-Alias Santiago A. , Ramirez-Campillo Rodrigo , Miras-Moreno Sergio , García-Pinillos Felipe , Marcos-Blanco Aitor
Published: March 2024

This study aimed to compare the acute effect of four back squat velocity-based training (VBT) protocols in terms of intensity (60% vs. 80% of the one repetition maximum [1RM]) and volume (10% vs. 30% threshold for velocity loss in the set) on the maximal aerobic speed (MAS) estimated from a running track test (RTT) in recreationally trained young adult men and women. Twenty participants (eleven men and nine women) undertook five randomized protocols in separate occasions: (i) RTT alone (control condition); (ii) VBT with 60% 1RM and a 10% velocity loss followed by RTT (VBT60–10 + RTT); (iii) VBT with 60% 1RM and a 30% velocity loss followed by RTT (VBT60–30 + RTT); (iv) VBT with 80% 1RM and 10% velocity loss followed by RTT (VBT80–10 + RTT); (v) VBT with 80% 1RM and 30% velocity loss followed by RTT (VBT80–30 + RTT). All VBT protocols involved three sets with three minutes of rest. The MAS was higher for RTT (control) than VBT60–30 + RTT (p < 0.001; Δ = 3.8%), VBT60–10 + RTT (p = 0.006; Δ = 2.8%), VBT80–10 + RTT (p = 0.008; Δ = 2.7%), and VBT80–30 + RTT (p = 0.019; Δ = 1.9%). No protocol × sex interaction was noted (p = 0.422). Therefore, regardless of sex, MAS is acutely impaired after VBT, especially if the training sets are performed with a low relative load and a high velocity loss threshold.
A Graph Convolutional Network Based on Sentiment Support for Aspect-Level Sentiment Analysis
Authors: Gao Ruiding , Jiang Lei , Zou Ziwei , Li Yuan , Hu Yurong
Published: March 2024

Aspect-level sentiment analysis is a research focal point for natural language comprehension. An attention mechanism is a very important approach for aspect-level sentiment analysis, but it only fuses sentences from a semantic perspective and ignores grammatical information in the sentences. Graph convolutional networks (GCNs) are a better method for processing syntactic information; however, they still face problems in effectively combining semantic and syntactic information. This paper presents a sentiment-supported graph convolutional network (SSGCN). This SSGCN first obtains the semantic information of the text through aspect-aware attention and self-attention; then, a grammar mask matrix and a GCN are applied to preliminarily combine semantic information with grammatical information. Afterward, the processing of these information features is divided into three steps. To begin with, features related to the semantics and grammatical features of aspect words are extracted. The second step obtains the enhanced features of the semantic and grammatical information through sentiment support words. Finally, it concatenates the two features, thus enhancing the effectiveness of the attention mechanism formed from the combination of semantic and grammatical information. The experimental results show that compared with benchmark models, the SSGCN had an improved accuracy of 6.33–0.5%. In macro F1 evaluation, its improvement range was 11.68–0.5%.
Performance Analysis and Improvement for CRUD Operations in Relational Databases from Java Programs Using JPA, Hibernate, Spring Data JPA
Authors: Bonteanu Alexandru Marius , Tudose Cătălin
Published: March 2024

The role of databases is to allow for the persistence of data, no matter if they are of the SQL or NoSQL type. In SQL databases, data are structured in a set of tables in the relational database model, grouped in rows and columns. CRUD operations (create, read, update, and delete) are used to manage the information contained in relational databases. Several dialects of the SQL language exist, as well as frameworks for mapping Java classes (models) to a relational database. The question is what we should choose for our Java application, and why? A comparison of the most frequently used relational database management systems, mixed with the most frequently used frameworks should give us some guidance about when to use what. The evaluation is conducted based on the time taken for each CRUD operation to run, from thousands to hundreds of thousands of entries, using the possible combinations in the relational database system and the framework. Aiming to assess and improve the performance, the experiments included the possibility of warming-up the Java Virtual Machine before the execution of queries. Also, the research investigated the time spent using different methods of code to determine the critical regions (bottlenecks). Thus, the conclusions provide a comprehensive overview of the performances of Java applications accessing databases depending on the suite decisions considering the database type, the framework in use, and the type of operation, with clear comparisons between the alternatives, the key findings of the advantages and drawbacks of each of them, and supporting architects and developers in their technological decisions and improving the speed of their programs.
Expected Changes in Heating and Cooling Degree Days over Greece in the near Future Based on Climate Scenarios Projections
Authors: Karagiannidis Athanasios , Lagouvardos Konstantinos , Kotroni Vassiliki , Galanaki Elisavet
Published: March 2024

The change in heating and cooling needs of Greece in the near future due to the climate change is assessed in the present study. Global and regional climate models and two different representative concentration pathways (RCPs) are used to simulate the expected change in temperature. A widely used methodology of computation of heating degree days (HDDs) and cooling degree days (CDDs) is employed with a base temperature of 18 °C. In agreement with the expected temperature rise in the near future, an HDD decrease and CDD increase under both RCPs is also expected. The changes under RCP8.5 are stronger compared to those under RCP4.5. Differences related to topography are noted. The HDD decrease is stronger than CDD increase but the relative increase in CDDs is higher than the relative increase in HDDs. The highest absolute decreases in HDDs are expected for February and March while the highest absolute increases in CDDs are expected during the three summer months.
Convolutional Forecasting of Particulate Matter: Toward a Data-Driven Generalized Model
Authors: Ferrari Luca , Guariso Giorgio
Published: March 2024

Air pollution poses a significant threat to human health and ecosystems. Forecasting the concentration of key pollutants like particulate matter can help support air quality planning and prevention measures. Deep learning methods are becoming increasingly popular for predicting air pollution and particulate matter concentration. Architectures like Convolutional Neural Networks can effectively account for the geographical features of the study domain. This work tests a Feed-Forward, a Long Short-Term Memory (LSTM), and a Convolutional Neural Network (CNN) on a polluted geographical domain in northern Italy. The best convolutional architecture was then implemented in two other quite different regions. The results show that the same CNN architecture provides remarkably accurate forecasts in all applications and that a network trained on PM10 data can accurately forecast PM2.5 concentrations up to 10 days ahead. These results suggest that the proposed CNN has high generalization capabilities and can thus be reliably used as a forecasting model for different areas.
Influence of Transboundary Pollution on the Variability of Surface Ozone Concentrations in the Desert Southwest of the U.S.: Case Study for Arizona
Authors: Betito Grace , Arellano Avelino , Sorooshian Armin
Published: March 2024

Arizona, a rapidly growing state in the southwestern U.S., faces ozone pollution challenges, including nonattainment areas in Yuma and Maricopa counties influenced by neighboring state pollution transport. In this study, we use five-year (2017–2021) hourly back-trajectories and O3 concentration data for concentration-weighted trajectory (CWT) analysis to identify transport pathways and potential source regions of O3 at six monitoring sites in Arizona. We divide the data into five seasons (winter, spring, dry summer, monsoon summer, and fall) to examine variations in O3 concentration and transport across sites and seasons. The highest mean O3 concentrations occur during spring (37–49 ppb), dry summer (39–51 ppb), and monsoon summer (34–49 ppb), while winter (19–41 ppb) exhibits the lowest seasonal mean. The CWT results reveal that high O3 concentrations (≥40 ppb) in Arizona, with the exception of Phoenix and Tucson sites, are influenced significantly by regional and international transport, especially in spring (14.9–35.4%) and dry summer (12.7–26.9%). The major potential source areas (excluding the Phoenix and Tucson sites) are predominantly located outside Arizona. This study highlights the critical role of pollution transport in influencing O3 variability within Arizona and will be valuable in shaping pollution control strategies in the future.
RETRACTED: Hosni Mahmoud, H.A. Diabetic Retinopathy Progression Prediction Using a Deep Learning Model. Axioms 2022, 11, 614
Authors: Hosni Mahmoud Hanan A.
Published: March 2024

The Axioms Editorial Office retracts the article Diabetic Retinopathy Progression Prediction Using a Deep Learning Model [...]
Immunomodulation of Cancer Cells Using Autologous Blood Concentrates as a Patient-Specific Cell Culture System: A Comparative Study on Osteosarcoma and Fibrosarcoma Cell Lines
Authors: Dohle Eva , Parkhoo Kamelia , Bennardo Francesco , Schmeinck Lena , Sader Robert , Ghanaati Shahram
Published: March 2024

The understanding that tumor cells might evade immunity through various mutations and the potential of an augmented immune system to eliminate abnormal cells led to the idea of utilizing platelet-rich fibrin (PRF), a blood concentrate containing the body’s immune elements as an adjunctive therapy for localized tumors. This study is the first that evaluated the effect of PRF generated with different relative centrifugal forces (RCFs) on osteoblastic and fibroblastic tumor cell lines MG63 and HT1080 with regard to cell viability, cytokine and growth factor release, and the gene expression of factors related to the cell cycle and apoptosis. Our findings could demonstrate decreased cell proliferation of MG63 and HT1080 when treated indirectly with PRF compared to cell cultures without PRF. This effect was more distinct when the cells were treated with low-RCF PRF, where higher concentrations of growth factors and cytokines with reduced RCFs can be found. Similar patterns were observed when assessing the regulation of gene expression related to the cell cycle and apoptosis in both MG63 and HT1080 cells treated with PRF. Despite variations, there was a consistent trend of an up-regulation of tumor-suppressive genes and a down-regulation of anti-apoptotic genes in both cell types following treatment with high- and, particularly, low-RCF PRF formulations.
ACNN-BiLSTM: A Deep Learning Approach for Continuous Noninvasive Blood Pressure Measurement Using Multi-Wavelength PPG Fusion
Authors: Cui Mou , Dong Xuhao , Zhuang Yan , Li Shiyong , Yin Shimin , Chen Zhencheng , Liang Yongbo
Published: March 2024

As an essential physiological indicator within the human body, noninvasive continuous blood pressure (BP) measurement is critical in the prevention and treatment of cardiovascular disease. However, traditional methods of blood pressure prediction using a single-wavelength Photoplethysmographic (PPG) have bottlenecks in further improving BP prediction accuracy, which limits their development in clinical application and dissemination. To this end, this study proposed a method to fuse a four-wavelength PPG and a BP prediction model based on the attention mechanism of a convolutional neural network and bidirectional long- and short-term memory (ACNN-BiLSTM). The effectiveness of a multi-wavelength PPG fusion method for blood pressure prediction was evaluated by processing PPG signals from 162 volunteers. The study compared the performance of the PPG signals with different individual wavelengths and using a multi-wavelength PPG fusion method in blood pressure prediction, assessed using mean absolute error (MAE), root mean squared error (RMSE) and AAMI-related criteria. The experimental results showed that the ACNN-BiLSTM model achieved a better MAE ± RMSE for a systolic BP and diastolic BP of 1.67 ± 5.28 and 1.15 ± 2.53 mmHg, respectively, when using the multi-wavelength PPG fusion method. As a result, the ACNN-BiLSTM blood pressure model based on multi-wavelength PPG fusion could be considered a promising method for noninvasive continuous BP measurement.
Immunohistochemical Characterization of Langerhans Cells in the Skin of Three Amphibian Species
Authors: Lombardo Giorgia Pia , Miller Anthea , Aragona Marialuisa , Messina Emmanuele , Fumia Angelo , Kuciel Michał , Alesci Alessio , Pergolizzi Simona , Lauriano Eugenia Rita
Published: March 2024

The amphibian taxon includes three orders that present different morphological characteristics: Anura, Caudata, and Apoda. Their skin has a crucial role: it acts as an immune organ constituting a physical, chemical, immunological, and microbiological barrier to pathogen insult and conducts essential physiological processes. Amphibians have developed specialized features to protect the vulnerable skin barrier, including a glandular network beneath the skin surface that can produce antimicrobial and toxic substances, thus contributing to the defense against pathogens and predators. This study aims to characterize Langerhans cells in the skin of Lithobates catesbeianus (order: Anura; Shaw, 1802), Amphiuma means (order: Caudata; Garden, 1821), and Typhlonectes natans (order: Apoda; Fischer, 1880) with the following antibodies: Langerin/CD207 (c-type lectin), Major Histocompatibility Complex (MHC)II, and Toll-like receptor (TLR)2 (expressed by different types of DCs). Our results showed Langerhans cells positive for Langerin CD/207 in the epidermis of the three species; moreover, some antigen-presenting cells (APCs) in the connective tissue expressed TLR2 and MHCII. The distribution of the Langerhans cells is very similar in the three amphibians examined, despite their different habitats. A greater knowledge of the amphibian immune system could be useful to better understand the phylogeny of vertebrates and to safeguard amphibians from population declines. Furthermore, the similarities between amphibians’ and human skin concerning immunological features may be useful in both biology and translational medicine.
Research Progress on Sesquiterpenoids of Curcumae Rhizoma and Their Pharmacological Effects
Authors: Cui Ting , Li Bo-Yu , Liu Fei , Xiong Liang
Published: March 2024

Curcumae Rhizoma, a traditional Chinese medicine with a wide range of pharmacological activities, is obtained from the dried rhizomes of Curcuma phaeocaulis VaL., Curcuma kwangsiensis S. G. Lee et C. F. Liang, and Curcuma wenyujin Y. H. Chen et C. Ling. Sesquiterpenoids and curcuminoids are found to be the main constituents of Curcumae Rhizoma. Sesquiterpenoids are composed of three isoprene units and are susceptible to complex transformations, such as cyclization, rearrangement, and oxidation. They are the most structurally diverse class of plant-based natural products with a wide range of biological activities and are widely found in nature. In recent years, scholars have conducted abundant studies on the structures and pharmacological properties of components of Curcumae Rhizoma. This article elucidates the chemical structures, medicinal properties, and biological properties of the sesquiterpenoids (a total of 274 compounds) isolated from Curcumae Rhizoma. We summarized extraction and isolation methods for sesquiterpenoids, established a chemical component library of sesquiterpenoids in Curcumae Rhizoma, and analyzed structural variances among sesquiterpenoids sourced from Curcumae Rhizoma of diverse botanical origins. Furthermore, our investigation reveals a diverse array of sesquiterpenoid types, encompassing guaiane-type, germacrane-type, eudesmane-type, elemane-type, cadinane-type, carane-type, bisabolane-type, humulane-type, and other types, emphasizing the relationship between structural diversity and activity. We hope to provide a valuable reference for further research and exploitation and pave the way for the development of new drugs derived from medicinal plants.
Synthesis and Validation of TRIFAPYs as a Family of Transfection Agents for Therapeutic Oligonucleotides
Authors: Isanta Berta , Delgado Ana , Ciudad Carlos J. , Busquets Mª Antònia , Griera Rosa , Llor Núria , Noé Véronique
Published: March 2024

Transfection agents play a crucial role in facilitating the uptake of nucleic acids into eukaryotic cells offering potential therapeutic solutions for genetic disorders. However, progress in this field needs the development of improved systems that guarantee efficient transfection. Here, we describe the synthesis of a set of chemical delivery agents (TRIFAPYs) containing alkyl chains of different lengths based on the 1,3,5-tris[(4-alkyloxy-1pyridinio)methyl]benzene tribromide structure. Their delivery properties for therapeutic oligonucleotides were evaluated using PolyPurine Reverse Hoogsteen hairpins (PPRHs) as a silencing tool. The binding of liposomes to PPRHs was evaluated by retardation assays in agarose gels. The complexes had a size of 125 nm as determined by DLS, forming well-defined concentrical vesicles as visualized by Cryo-TEM. The prostate cancer cell line PC-3 was used to study the internalization of the nanoparticles by fluorescence microscopy and flow cytometry. The mechanism of entrance involved in the cellular uptake was mainly by clathrin-mediated endocytosis. Cytotoxicity analyses determined the intrinsic toxicity caused by each TRIFAPY and the effect on cell viability upon transfection of a specific PPRH (HpsPr-C) directed against the antiapoptotic target survivin. TRIFAPYs C12-C18 were selected to expand these studies in the breast cancer cell line SKBR-3 opening the usage of TRIFAPYs for both sexes and, in the hCMEC/D3 cell line, as a model for the blood–brain barrier. The mRNA levels of survivin decreased, while apoptosis levels increased upon the transfection of HpsPr-C with these TRIFAPYs in PC-3 cells. Therefore, TRIFAPYs can be considered novel lipid-based vehicles for the delivery of therapeutic oligonucleotides.
DPP Inhibition Enhances the Efficacy of PD-1 Blockade by Remodeling the Tumor Microenvironment in Lewis Lung Carcinoma Model
Authors: Lei Mengrong , Liu Junyan , Gao Ying , Dai Wenting , Huang Hanxue , Jiang Qingqing , Liu Zhaoqian
Published: March 2024

The remarkable efficacy of cancer immunotherapy has been established in several tumor types. Of the various immunotherapies, PD-1/PD-L1 inhibitors are most extensively used in the treatment of many cancers in clinics. These inhibitors restore the suppressed antitumor immune response and inhibit tumor progression by blocking the PD-1/PD-L1 signaling. However, the low response rate is a major limitation in the clinical application of PD-1/PD-L1 inhibitors. Therefore, combination strategies that enhance the response rate are the need of the hour. In this investigation, PT-100 (also referred to as Talabostat, Val-boroPro, and BXCL701), an orally administered and nonselective dipeptidyl peptidase inhibitor, not only augmented the effectiveness of anti-PD-1 therapy but also significantly improved T immune cell infiltration and reversed the immunosuppressive tumor microenvironment. The combination of PT-100 and anti-PD-1 antibody increased the number of CD4+ and CD8+ T cells. Moreover, the mRNA expression of T cell-associated molecules was elevated in the tumor microenvironment. The results further suggested that PT-100 dramatically reduced the ratio of tumor-associated macrophages. These findings provide a promising combination strategy for immunotherapy in lung cancer.
Triptolide Administration Alters Immune Responses to Mitigate Insulin Resistance in Obese States
Authors: Grodsky Lyudmila , Wilson Mickey , Rathinasabapathy Thirumurugan , Komarnytsky Slavko
Published: March 2024

Individuals who are overweight or obese are at increased risk of developing prediabetes and type 2 diabetes, yet the direct molecular mechanisms that connect diabetes to obesity are not clear. Chronic, sustained inflammation is considered a strong risk factor in these interactions, directed in part by the short-lived gene expression programs encoding for cytokines and pro-inflammatory mediators. In this study, we show that triptolide administration in the C57BL/6 diet-induced obese mice at up to 10 μg/kg/day for 10 weeks attenuated the development of insulin resistance and diabetes, but not obesity, in these animals. Significant reductions in adipose tissue inflammation and improved insulin sensitivity were observed in the absence of changes in food intake, body weight, body composition, or energy expenditure. Analysis of the core cluster of biomarkers that drives pro-inflammatory responses in the metabolic tissues suggested TNF-α as a critical point that affected the co-development of inflammation and insulin resistance, but also pointed to the putatively protective roles of increased COX-2 and IL-17A signaling in the mediation of these pathophysiological states. Our results show that reduction of diet-induced inflammation confers partial protection against insulin resistance, but not obesity, and suggest the possibility of achieving overweight phenotypes that are accompanied by minimal insulin resistance if inflammation is controlled.
Artificial Intelligence Applied to Electrical and Non-Invasive Hemodynamic Markers in Elderly Decompensated Chronic Heart Failure Patients
Authors: Piccirillo Gianfranco , Moscucci Federica , Mezzadri Martina , Caltabiano Cristina , Cisaria Giovanni , Vizza Guendalina , De Santis Valerio , Giuffrè Marco , Stefano Sara , Scinicariello Claudia , Carnovale Myriam , Corrao Andrea , Lospinuso Ilaria , Sciomer Susanna , Rossi Pietro
Published: March 2024

Objectives: The first aim of this study was to assess the predictive power of Tend interval (Te) and non-invasive hemodynamic markers, based on bioimpedance in decompensated chronic heart failure (CHF). The second one was to verify the possible differences in repolarization and hemodynamic data between CHF patients grouped by level of left ventricular ejection fraction (LVEF). Finally, we wanted to check if repolarization and hemodynamic data changed with clinical improvement or worsening in CHF patients. Methods: Two hundred and forty-three decompensated CHF patients were studied by 5 min ECG recordings to determine the mean and standard deviation (TeSD) of Te (first study). In a subgroup of 129 patients (second study), non-invasive hemodynamic and repolarization data were recorded for further evaluation. Results: Total in-hospital and cardiovascular mortality rates were respectively 19 and 9%. Te was higher in the deceased than in surviving subjects (Te: 120 ± 28 vs. 100 ± 25 ms) and multivariable logistic regression analysis reported that Te was related to an increase of total (χ2: 35.45, odds ratio: 1.03, 95% confidence limit: 1.02–1.05, p < 0.001) and cardiovascular mortality (χ2: 32.58, odds ratio: 1.04, 95% confidence limit: 1.02–1.06, p < 0.001). Subjects with heart failure with reduced ejection fraction (HFrEF) reported higher levels of repolarization and lower non-invasive systolic hemodynamic data in comparison to those with preserved ejection fraction (HFpEF). In the subgroup, patients with the NT-proBNP reduction after therapy showed a lower rate of Te, heart rate, blood pressures, contractility index, and left ventricular ejection time in comparison with the patients without NT-proBNP reduction. Conclusion: Electrical signals from ECG and bioimpedance were capable of monitoring the patients with advanced decompensated CHF. These simple, inexpensive, non-invasive, easily repeatable, and transmissible markers could represent a tool to remotely monitor and to intercept the possible worsening of these patients early by machine learning and artificial intelligence tools.