bluuebunny/crossref_metadata_2025 huggingface dataset:
Valid knowledge of performance provided by a motion capturing system in shot put
Authors: Künzell Stefan , Knoblich Anna , Stippler Annika
Published: January 2025

Extended feedback on knowledge of performance in sports techniques is very challenging and requires a high level of expertise. This poses a significant problem for experiments on providing extended feedback, as it is essential to ensure that the “correct” feedback is given for it to be effective. In this study, we investigate whether the correct feedback can be determined based on kinematic data. Ten participants and one model were recorded during shot put using a Motion Capturing (MoCap) system and simultaneously captured on video. The videos were analysed by two experts, and the two most critical errors were noted. By qualitatively comparing the deviations of the participants from the model, the experts’ error feedback was identified in the motion curves of the MoCap system. The expert feedback for two participants was sealed in an envelope. In a qualitative analysis of the motion data, the error feedback was then determined and subsequently compared with the experts’ feedback. These error feedbacks largely matched. It was shown that, in principle, it is possible to extract errors from the kinematic angle and distance curves of the movement. This study opens the door to an automated version of the qualitative assessment of movements by AI. Further research can now focus on the topic of conveying AI-generated feedback. This could then also provide a valid foundation for experiments on the effects of knowledge of performance.
Isolation, screening and in-vitro characterization of plant growth-promoting Bradyrhizobium isolates from the nodules of Bambara groundnut (Vigna subterranean) for potential use as bioinoculants
Authors: Seidu Osman Abdulai , Githiri Stephen Mwangi , Wesonga John Mwibanda , Ngumi Victoria Wambui
Published: January 2025

Bambara groundnut (Vigna subterranean) is a resilient leguminous crop that can withstand drought conditions and is commonly grown in arid regions where soils are degraded and of low fertility status. Although the crop can fix nitrogen, its yields frequently fall short of their maximum potential which can be attributed to association with ineffective rhizobia strains. In the present study, we isolated, screened, and in-vitro characterized Bradyrhizobium isolates from the root nodules of Bambara groundnuts with plant growth promoting properties for potential use as bioinoculant. Root nodules were sampled from JKUAT farm where healthy Bambara groundnut plants were growing. Ten isolates with slow growth rate were isolated. The 10 isolates were screened using morphological, biochemical and molecular (16S rRNA gene sequencing) techniques. The sequence analysis established that all isolates were congeneric with Bradyrhizobium. In addition, all the isolates showed Nitrogen-fixing potentials and also had significant (p < 0.005) ability to solubilize phosphate in the range of 0.77 ± 0.771–3.22 ± 0.368 phosphate solubilizing index. Further, isolates P4A17, P4A18, P4A16, P4A6 and C2 produced IAA concentrations of 54.97 ± 3.21–108 ± 12.10 μg/mL. However, none of the isolates could produce HCN. The isolates were assessed further on their ability to grow in varied physiological conditions. Among them, P4A6 and P4A18 demonstrated higher growth potential under pH 3, pH 5, pH 9, pH 11, 1% NaCl, 3% NaCl, 5% NaCl and high temperature range of 40°C–50°C than the rest of the isolates. Given the in-vitro results, these isolates are promising bioinoculant (biofertilizer) candidates and, ought to be further tested in greenhouse and field conditions for Bambara groundnut production.
The influence of digital village construction on agricultural green development-based on the mediate role of industrial structure upgrading
Authors: Li Zhaoguang , Hu Kai , Shi Qinying
Published: January 2025

Introduction

Leading the green transformation of agriculture through the digital economy is a crucial step toward high-quality agricultural growth. The role and mechanism of China's digital rural construction in promoting the green development of the whole process of agricultural production needs further analysis.

Methods

Based on the panel data of prefecture-level cities in Henan Province from 2013 to 2022, we used the entropy topsis model, the fixed effect model, the Tobit regression model, the intermediate effect model, and the threshold effect model, aiming to explore the effects of digital village construction on agricultural green development and their regional differences, impacts paths and non-linear effects.

Results

The study found the following: (1) The level of agricultural green development and the digital village construction have increased in Henan province, with the latter exhibiting a spatial clustering phenomena. (2) Digital village construction has a significant positive effect on green agricultural development, but this effect does not exist in mountainous areas. (3) The digital village construction can impact green agricultural development through the updating of agricultural structures. (4) The positive influence of digital village construction on green agricultural development is non-linear, with a single-threshold and increasing marginal effects.

Discussion

These findings provide references for other countries to use digital elements to promote green and sustainable development of agriculture. The main contribution of this study is to explore the role of agricultural industrial structure upgrading in the impact of digital rural construction on agricultural green development.
Does tillage system affect agricultural production and farmers’ incomes? Evidence from 234 typical farms in 29 countries
Authors: Wang Jiamei , Zhou Hui , Hu Xiangdong
Published: January 2025

Investigating the impact of tillage system on agricultural production and farmer income, particularly in the face of global threats to food security and adverse environmental conditions, holds considerable importance for guiding policy formulation. This study employs a stochastic frontier analysis to estimate the technical efficiency of wheat production, utilizing data from 234 typical farms across 29 countries for the period 2012–2022. The technical efficiency range for wheat production varies from 0.257 to 0.976, with an average value of 0.830. Additionally, the impact of tillage system on technical efficiency and grain revenue is examined using propensity score matching. The findings indicate that there is no significant difference in technical efficiency between conservation tillage and traditional tillage. Furthermore, compared to conservation tillage, traditional tillage leads to higher grain revenue. Policymakers should evaluate technological adoption comprehensively, considering economic, social, and environmental aspects. Special attention should be directed toward assessing the economic benefits for farmers.
New technologies for safe practice and training during pregnancy: a gynecologist and forensic pathologist perspective narrative review
Authors: Capasso Emanuele , Marisei Mariagrazia , Aquino Carmen Imma , Di Lorenzo Pierpaolo , Casella Claudia
Published: January 2025

This paper investigates the intersection of gender health equity within two distinct medical residency programs: obstetrics &gynecology and forensic pathology. It specifically addresses the unique challenges that young female residents encounter during pregnancy, emphasizing the Italian regulatory frameworks and recommendations pertinent to both fields. The online search for this narrative review was based on keywords such as: “medicine” AND “education” AND “pregnancy” on the main online scientific database along with “metaverse” AND “education” AND “pregnancy”. The analysis reveals the heightened risks faced by female medical doctors in surgical and forensic environments, underlining the need for targeted support. Additionally, this study explores the perceived potential of augmented reality (AR) and the metaverse as innovative solutions to mitigate biological risks, thereby enabling pregnant surgeons to maintain effective practice during this critical period. The findings obtained from a literature review aim to contribute to ongoing discussions about improving gender equity in medical training and practice, regarding the surgical and forensic field where the temporary stopping period may result in a different surgical learning curve compared to that of male colleagues.
Surveillance of Mycoplasma agassizii in Texas tortoises (Gopherus berlandieri) for translocation with emphasis on treatment and recovery
Authors: Moeller Christin A. , Perales Saren , Rodriguez Wraith , Martin Alynn M. , Eversole Cord B. , Rideout-Hanzak Sandra , Crump Paul , Hilton Clayton D. , Henke Scott E.
Published: January 2025

Background

Texas tortoises (Gopherus berlandieri) are a Texas-state threatened species. Translocation is often suggested as a mitigation option; however, disease status and the potential for spread must be considered prior to such efforts. Mycoplasma infection of the upper respiratory tract is a concern within tortoise populations, which requires monitoring so translocation efforts do not inadvertently spread the disease.

Objectives

We determined and compared the prevalences of Mycoplasma agassizii in Texas tortoises from donor and recipient sites in southern Texas prior to translocation, treated Mycoplasma agassizii-infected tortoises with danofloxacin, and developed alternate Mycoplasma agassizii treatments for Texas tortoises.

Methods

We collected 171 and 23 Texas tortoises from a 270-ha and a 100-ha donor site and recipient site, respectively. We began a regimen of danofloxacin (6 mg/kg body weight injected subcutaneously every other day for 30 days) for tortoises with clinical signs (N = 20). We noted an additional 10 tortoises began displaying clinical signs of upper respiratory tract disease (URTD) after translocation, so we designed a trial to test tulathromycin (5 mg/kg body weight given intramuscularly once/week for 7 weeks) or oxytetracycline (8 mg/kg body weight given subcutaneously once/day for 14 days) as Mycoplasma treatments for symptomatic tortoises.

Results

Within the donor and recipient sites, 56 (32.7%) and 8 (34.8%), respectively, had antibody titers suggestive of past exposure. Eighteen tortoises from the donor site (10.5%) and 2 from the recipient site (8.7%) displayed clinical signs (i.e., clear serous nasal discharge, conjunctivitis, and palpebral edema) consistent with Mycoplasmal URTD upon initial collection, even though all polymerase chain reaction (PCR) results were negative for active shedding of Mycoplasma agassizii. We ceased treatment after the first dose of danofloxacin due to adverse reactions, which only began to subside after 72 h from the initial dose. Neither tulathromycin or oxytetracycline caused the clinical signs of URTD to subside after a 50-day treatment period.

Conclusion

Mycoplasma is a persistent issue facing Texas tortoises. Stressors, such as translocation, can cause Mycoplasma-seropositive tortoises to display clinical symptoms of URTD, which can abate without treatment, once the stressor subsides.

Implications

Danofloxacin, the recommended treatment for Mycoplasma infection in tortoises, is too potent for Texas tortoises.
One-year surveillance of Chlamydia spp. infection in stray cats from northeastern Italy
Authors: Bellinati Laura , Ceglie Letizia , Mazzotta Elisa , Campalto Mery , Lucchese Laura , Natale Alda
Published: January 2025

Stray cats potentially act as reservoir for zoonotic agents, posing a risk of exposure to humans and domestic cats. The most prevalent Chlamydiaceae species in cats is Chlamydia (C.) felis, which is frequently associated with conjunctivitis and/or upper respiratory disease. The zoonotic potential of C. felis is believed to be relatively low, although exposure is possible through handling infected cats, by contact with their aerosol, and via fomites. Infection is more frequent in conditions of overcrowding, stress, poor hygiene and impairment of the immune system. For this reason, stray cats appear to be particularly susceptible to this pathogen. Aim of the study was to identify the molecular occurrence of Chlamydiaceae in stray and colony cats. Between May 2021 and June 2022, in seven provinces of northeastern Italy, veterinary services officers collected oropharyngeal swabs from 379 stray and colony cats. The samples were screened for Chlamydiaceae by real-time PCR targeting a 23S gene fragment. Positive samples were further analyzed either by a C. felis-specific qPCR or by amplification and sequencing of a 16S rRNA gene fragment. Overall, 7.7% of the cats tested positive for Chlamydia spp., and all were identified as C. felis. Among the positive individuals, only one exhibited respiratory symptoms. The analysis of anamnestic data revealed a significantly higher frequency of C. felis in male intact cats during the spring season, suggesting a potential behavioral aspect of this infection. Although the zoonotic risk of this Chlamydia species is low, it would be prudent to exercise caution when handling stray cats.
Mixing Data Cube Architecture and Geo-Object-Oriented Time Series Segmentation for Mapping Heterogeneous Landscapes
Authors: Chaves Michel E. D. , Soares Lívia G. D. , Barros Gustavo H. V. , Pessoa Ana Letícia F. , Elias Ronaldo O. , Golzio Ana Claudia , Conceição Katyanne V. , Morais Flávio J. O.
Published: January 2025

The conflict between environmental conservation and agricultural production highlights the need for precise land use and land cover (LULC) mapping to support agro-environmental-related policies. Satellite image time series from the Moderate Resolution Image Spectroradiometer (MODIS) sensor are essential for current LULC mapping efforts. However, most approaches focus on pixel data, and studies exploring object-based spatiotemporal heterogeneity and correlation features in its time series are limited. The objective of this study is to mix the data cube architecture (analysis-ready data—ARD) and the geo-object-oriented time series segmentation via Geographic Object-Based Image Analysis (GEOBIA) to assess its performance in identifying natural vegetation and double-cropping practices over a crop season. The study area was the state of Mato Grosso, Brazil. Results indicate that, by combining GEOBIA and time series analysis (materialized by the multiresolution segmentation algorithm to derive spatiotemporal geo-objects of the MODIS data cube), representative training data collected after a quality control process, and the Support Vector Machine to classify the ARD, the overall accuracy was 0.95 and all users’ and producers’ accuracies were higher than 0.88. By considering the heterogeneity of Mato Grosso’s landscape, the results indicate the potential of the approach to provide accurate mapping.
The Grouping Patterns of Cervus canadensis songaricus in the Tianchi Bogda Peak Nature Reserve of Tianshan Mountain, Northwestern China
Authors: Ma Xuejun , Blank David , Xu Feng
Published: January 2025

Grouping behavior is a fundamental component of animal population dynamics, shaped by both environmental factors and the social interactions among individuals, ultimately enhancing adaptability to their habitats. However, the social organization of the wapiti subspecies inhabiting the Tian Shan Mountains remains poorly understood. In this study, we investigated the grouping behavior of Tianshan wapiti in the Bogda Peak Nature Reserve, northwestern China from July 2019 to November 2020. Using infrared-triggered camera traps, we analyzed seasonal variation in group size and group frequency, as well as sex-specific aggregation and segregation pattern. Our results showed that Tianshan wapiti group sizes varied considerably, ranging from 1 to 32 individuals, with most groups being relatively smaller, typically consisting of 1 to 9 individuals. Seasonal differences in group size were significant (H = 28.91, df = 3, p < 0.05). Additionally, group type showed significant variation across seasons (χ2 = 93.32, df = 18, p < 0.05). Anakysis of sex-specifia aggregation and segregation (SSAS) indicated no consistent segregation of females and males throughout the year. However, there was a notable tendency for sex-specific aggregation during July and from October to December. These findings provide new insights into the social organization and seasonal grouping behavior of Tianshan wapiti, contributing to a better understanding of their behavioral ecology and informing conservation and management strategies for this subspecies.
Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area
Authors: Tariku Girma , Ghiglieno Isabella , Sanchez Morchio Andres , Facciano Luca , Birolleau Celine , Simonetto Anna , Serina Ivan , Gilioli Gianni
Published: January 2025

Land cover mapping is essential to understanding global land-use patterns and studying biodiversity composition and the functioning of eco-systems. The introduction of remote sensing technologies and artificial intelligence models made it possible to base land cover mapping on satellite imagery in order to monitor changes, assess ecosystem health, support conservation efforts, and reduce monitoring time. However, significant challenges remain in managing large, complex satellite imagery datasets, acquiring specialized datasets due to high costs and labor intensity, including a lack of comparative studies for the selection of optimal deep learning models. No less important is the scarcity of aerial datasets specifically tailored for agricultural areas. This study addresses these gaps by presenting a methodology for semantic segmentation of land covers in agricultural areas using satellite images and deep learning models with pre-trained backbones. We introduce an efficient methodology for preparing semantic segmentation datasets and contribute the “Land Cover Aerial Imagery” (LICAI) dataset for semantic segmentation. The study focuses on the Franciacorta area, Lombardy Region, leveraging the rich diversity of the dataset to effectively train and evaluate the models. We conducted a comparative study, using cutting-edge deep-learning-based segmentation models (U-Net, SegNet, DeepLabV3) with various pre-trained backbones (ResNet, Inception, DenseNet, EfficientNet) on our dataset acquired from Google Earth Pro. Through meticulous data acquisition, preprocessing, model selection, and evaluation, we demonstrate the effectiveness of these techniques in accurately identifying land cover classes. Integrating pre-trained feature extraction networks significantly improves performance across various metrics. Additionally, addressing challenges such as data availability, computational resources, and model interpretability is essential for advancing the field of remote sensing, in support of biodiversity conservation and the provision of ecosystem services and sustainable agriculture.
Detection of Deaths Caused by Hyperkalemia
Authors: Żulicka Małgorzata , Sobczak Kamila , Kowalczyk Dominik , Sikorska Sylwia , Arendt Wioletta , Hałas-Wiśniewska Marta
Published: January 2025

Under normal conditions, potassium is predominantly found within cells. The concentration gradient of sodium and potassium ions between intracellular and extracellular spaces enables signal transmission through membrane depolarization. The disruption of this transcellular process leads to elevated potassium ion levels in the extracellular space, and thus in the blood, a condition known as hyperkalemia. Clinically, hyperkalemia may present as cardiac arrhythmias, muscle weakness, and palpitations. The post-mortem accumulation of potassium ions in various human tissues and organs, such as the heart, liver, kidneys, lungs, and vitreous body, particularly in cases of overdose, has been an area of research interest for years. Unfortunately, deaths caused by hyperkalemia are difficult to identify due to their non-specific symptoms and are often misinterpreted as cardiovascular-related. Furthermore, most potassium ion concentration tests developed in recent years are non-specific, have limitations, or are based on outdated techniques. Consequently, alternative methods, such as histopathological tissue analysis, potassium concentration assessment in the vitreous body, and aldosterone level measurement, show promise for improving the post-mortem detection of exogenous hyperkalemia.
Testing the Purity of Limnospira fusiformis Cultures After Axenicity Treatments
Authors: Schagerl Michael , Kaptejna Alexander , Polz Fabian , Ali Sameh S. , Huo Shuhao , Seneca Joana , Pjevac Petra , Hechtl Vera
Published: January 2025

Contaminations are challenging for monocultures, as they impact the culture conditions and thus influence the growth of the target organism and the overall biomass composition. In phycology, axenic cultures comprising a single living species are commonly strived for both basic research and industrial applications, because contaminants reduce significance for analytic purposes and interfere with the safety and quality of commercial products. We aimed to establish axenic cultures of Limnospira fusiformis, known as the food additive “Spirulina”. Axenicity is strived because it ensures that pathogens or harmful microorganisms are absent and that the harvested biomass is consistent in terms of quality and composition. For the axenic treatment, we applied sterile filtration, ultrasonication, pH treatment, repeated centrifugation, and administration of antibiotics. For testing axenicity, we considered the most common verification method plate tests with Lysogeny Broth (LB) medium, which indicated axenicity after treatments were performed. In addition, we included plate tests with Reasoner’s 2A (R2A) agar and modified Zarrouk+ medium, the latter comparable to the biochemical properties of L. fusiformis’ cultivation medium. In contrast to LB plates, the other media, particularly Zarrouk+, indicated bacterial contamination. We conclude that LB-agar plates are inappropriate for contamination screening of extremophiles. Contamination was also verified by cultivation-independent methods like flow cytometry and 16S rRNA genome amplicon sequencing. We detected taxa of the phyla Proteobacteria, Bacteriodota, Firmicutes and to a lesser extent Verrucomicrobiota. Contaminants are robust taxa, as they survived aggressive treatments. Sequencing data suggest that some of them are promising candidates for in-depth studies to commercially exploit them.
Assessment of the Wettability and Mechanical Properties of Stearic-Acid-Modified Hydrophobic Cementitious Materials
Authors: Wang Xuhao , Zhang Wenxiao , Wang Yuan , Wu Hongke , Danzeng Dunzhu , Meng Yahong
Published: January 2025

Moisture is a critical factor leading to the deterioration of concrete structures. Hydrophobic cement-based materials, with their excellent waterproof performance, hold significant application value in humid, coastal, and cold environments. This study employed stearic acid (STA, CH3(CH2)16COOH) as a hydrophobic agent dissolved in anhydrous ethanol using ultrasonication to create an STA–ethanol solution. In addition, the ball-milling method was used to mix STA with tuff powder (TP) to prepare hydrophobic modified tuff powder (MTP). This study investigated the effects of the STA content, water–cement (w/c) ratio, cement–sand (c/s) ratio, the replacement rate, and addition method of TP and MTP on the wettability (contact angle and sorptivity) and compressive strength of the mortar. The effects of the STA on the cement hydration were explored by microanalysis techniques, such as SEM, XRD, and FTIR, and the modification method with the best effect was recommended based on a gray correlation degree analysis. The results indicate that the STA could be successfully grafted into the mortar without affecting the types of cement hydration products. When using the STA–ethanol solution for hydrophobic modification, adding 0.9% STA by weight increased the mortar contact angle to 69.5° and reduced the sorptivity by 22%, while the 28-day compressive strength was decreased. When the w/c ratio was 0.5, the contact angle rose with the increase in the replacement rate of MTP, while the sorptivity and compressive strength decreased. The grey relational analysis showed that at a w/c ratio of 0.4, the STA–ethanol solution was a more effective modification method in terms of reducing the mortar sorptivity.
Karyotype Analysis and Genome Size Estimation Using Flow Cytometry of the Genus Encyclia Hook. (Orchidaceae: Laeliinae)
Authors: Zhou Chengcheng , Leng Qingyun , Lu Jinping , Xu Shisong , Peng Donghui , Yin Junmei , Niu Junhai
Published: January 2025

Encyclia Hook. is a tropical orchid known for its delightful fragrance and distinctive flower shapes, which have been widely used as superior parents for inter and intrageneric breeding. In this study, karyotype analysis shows that the number of chromosomes in diploid plants of Encyclia is 40, with all species exhibiting mesial and central filamentous chromosomes. The karyotype of diploid plants is primarily 2B. Among the three detected intergeneric hybrids, both Enanthleya Circus Lady ‘Coastal Star’ and Guaricyclia Kyoguchi ‘Fumi’ are triploid, while Robertsara ‘Green Wonder’ is tetraploid. By evaluating various lysates and tissues, a flow cytometry system (FCM) is developed with Galbraith’s buffer (GLB) and tender leaves. By using Dendrobium officinale as an external reference, the genome size of the Encyclia species and its intra and intergeneric hybrids are assessed, which ranges from 1.07 Gb to 3.23 Gb. These results will establish a foundation for high-throughput identification of germplasms, advance crossbreeding efforts, and enhance the understanding of the genome in Encyclia.
Marked Gingival Overgrowth Protruding from the Oral Cavity Due to Sodium Valproate
Authors: Uegami Mami , Ito Hiroaki , Shiohama Tadashi
Published: January 2025

Drug-induced gingival overgrowth is associated with various systemic diseases, including epilepsy. Among antiepileptic medications, phenytoin is commonly reported to cause this condition. In contrast, sodium valproate (VPA), another widely used antiepileptic drug, rarely induces gingival overgrowth. This difference in side effects highlights the variability in drug-induced oral complications among different antiepileptic medications. This case study presents a patient who developed significant gingival overgrowth after using VPA for over 10 years. The study aims to identify VPA as the causative agent and observe changes during long-term administration and after dose reduction. Our findings demonstrate that even long-standing gingival overgrowth can improve rapidly following discontinuation of the causative medication, providing valuable insights for managing similar cases in the future.
Deep Transfer Learning for Classification of Late Gadolinium Enhancement Cardiac MRI Images into Myocardial Infarction, Myocarditis, and Healthy Classes: Comparison with Subjective Visual Evaluation
Authors: Ben Khalifa Amani , Mili Manel , Maatouk Mezri , Ben Abdallah Asma , Abdellali Mabrouk , Gaied Sofiene , Ben Ali Azza , Lahouel Yassir , Bedoui Mohamed Hedi , Zrig Ahmed
Published: January 2025

Background/Objectives: To develop a computer-aided diagnosis (CAD) method for the classification of late gadolinium enhancement (LGE) cardiac MRI images into myocardial infarction (MI), myocarditis, and healthy classes using a fine-tuned VGG16 model hybridized with multi-layer perceptron (MLP) (VGG16-MLP) and assess our model’s performance in comparison to various pre-trained base models and MRI readers. Methods: This study included 361 LGE images for MI, 222 for myocarditis, and 254 for the healthy class. The left ventricle was extracted automatically using a U-net segmentation model on LGE images. Fine-tuned VGG16 was performed for feature extraction. A spatial attention mechanism was implemented as a part of the neural network architecture. The MLP architecture was used for the classification. The evaluation metrics were calculated using a separate test set. To compare the VGG16 model’s performance in feature extraction, various pre-trained base models were evaluated: VGG19, DenseNet121, DenseNet201, MobileNet, InceptionV3, and InceptionResNetV2. The Support Vector Machine (SVM) classifier was evaluated and compared to MLP for the classification task. The performance of the VGG16-MLP model was compared with a subjective visual analysis conducted by two blinded independent readers. Results: The VGG16-MLP model allowed high-performance differentiation between MI, myocarditis, and healthy LGE cardiac MRI images. It outperformed the other tested models with 96% accuracy, 97% precision, 96% sensitivity, and 96% F1-score. Our model surpassed the accuracy of Reader 1 by 27% and Reader 2 by 17%. Conclusions: Our study demonstrated that the VGG16-MLP model permits accurate classification of MI, myocarditis, and healthy LGE cardiac MRI images and could be considered a reliable computer-aided diagnosis approach specifically for radiologists with limited experience in cardiovascular imaging.
Posterior Urethral Valves and Fertility: Insight on Paternity Rates and Seminal Parameters
Authors: Della Corte Marcello , Gerocarni Nappo Simona , Aversa Antonio , La Vignera Sandro , Porpiglia Francesco , Fiori Cristian , Mondaini Nicola
Published: January 2025

Background: Posterior urethral valves (PUVs) represent the most common cause of male congenital lower urinary tract obstruction, often responsible for renal dysplasia and chronic renal failure. Despite recent improvements in patients’ outcomes thanks to prenatal ultrasound early diagnosis, PUVs can still impact sexual function and fertility. This study aims to review the available evidence on fertility in PUV patients, examining paternity rates and semen parameters. Methods: A review was conducted of the PubMed, Cochrane, Scopus, and Embase databases. Studies focusing on fertility and paternity outcomes in PUV patients were selected, including case reports, case series, and retrospective and prospective studies. Results: A total of 15 studies met the inclusion criteria. The review revealed that PUV patients often exhibit compromised semen parameters, including low sperm count, reduced motility, and abnormal morphology, as well as alterations in seminal plasma. PUV diagnoses are common in adults exhibiting infertility and ejaculation disorders, suggesting PUVs cannot be considered only a pediatric disease. Paternity rates among PUV patients were rarely reported in extenso, hampering the correct assessment of the overall medium paternity rate and its comparison with that of healthy individuals. Lastly, seminal parameters were assessed in a minimal cohort of patients, therefore, they could not be considered representative. Conclusions: Fertility and seminal parameters in PUV patients represent an under-investigated area. PUVs can variably and non-univocally affect fatherhood, and they may be associated with compromised semen quality. Early intervention and long-term follow-up are essential to address potential fertility issues. Future research should focus on developing targeted strategies to preserve and enhance fertility in this patient population.
Bidimensional Increment Entropy for Texture Analysis: Theoretical Validation and Application to Colon Cancer Images
Authors: Abid Muqaddas , Hitam Muhammad Suzuri , Ali Rozniza , Azami Hamed , Humeau-Heurtier Anne
Published: January 2025

Entropy algorithms are widely applied in signal analysis to quantify the irregularity of data. In the realm of two-dimensional data, their two-dimensional forms play a crucial role in analyzing images. Previous works have demonstrated the effectiveness of one-dimensional increment entropy in detecting abrupt changes in signals. Leveraging these advantages, we introduce a novel concept, two-dimensional increment entropy (IncrEn2D), tailored for analyzing image textures. In our proposed method, increments are translated into two-letter words, encoding both the size (magnitude) and direction (sign) of the increments calculated from an image. We validate the effectiveness of this new entropy measure by applying it to MIX2D(p) processes and synthetic textures. Experimental validation spans diverse datasets, including the Kylberg dataset for real textures and medical images featuring colon cancer characteristics. To further validate our results, we employ a support vector machine model, utilizing multiscale entropy values as feature inputs. A comparative analysis with well-known bidimensional sample entropy (SampEn2D) and bidimensional dispersion entropy (DispEn2D) reveals that IncrEn2D achieves an average classification accuracy surpassing that of other methods. In summary, IncrEn2D emerges as an innovative and potent tool for image analysis and texture characterization, offering superior performance compared to existing bidimensional entropy measures.
Knowledge Sharing in Swarms Realized by Artificially Empathetic Communication
Authors: Siwek Joanna , Żywica Patryk , Pierzyński Konrad , Siwek Przemysław , Wójcik Adrian
Published: January 2025

In this paper, we present a novel method of communicating knowledge between units in a swarm, applying artificial empathy and imprecise cognition to improve cooperation and adaptation. This research focuses on building a swarming agent’s individual knowledge base, relying on knowledge imprecisely shared by another unit and one’s own experience. The fuzzy similarity is used to fill the missing parameters, a subjective subset of which describes each agent. The obtained knowledge is then implemented as a particular set of actions, distinctive for each swarming agent. The actions bring the swarming unit closer to the desired state, appointed with the use of uncertainty combined with imprecision. Each agent compares their future possibilities with other’s prospects and decides whether to help, continue with their own action, or learn new knowledge from others.
Detecting Defects in Solar Panels Using the YOLO v10 and v11 Algorithms
Authors: Ghahremani Ali , Adams Scott D. , Norton Michael , Khoo Sui Yang , Kouzani Abbas Z.
Published: January 2025

Solar panels play a crucial role in producing renewable electricity power for the grid, and this role grows more significant each year. However, defects in solar panels can significantly drop power output, leading to grid instability. Therefore, employing an efficient Artificial Intelligence (AI) algorithm to autonomously detect defects in solar panels is crucial. In this study, we employ the You Only Look Once (YOLO) v9, v10, and v11 algorithms to detect defects in solar panels. To this end, we examined their performance results via training on three datasets. The first dataset includes 191 thermal images with an image size of 200 × 160 pixels to identify a cell, multi-cell levels, and shadow defects. The second dataset consists of 792 optical images of solar panels with an image size of 244 × 244 to identify dust, snow, bird droppings, physical damage, and electrical defects. The third dataset includes 316 thermal images with an image size of 200 × 160 pixels, an enhanced version of the first dataset. Moreover, we examined the training and test performance results of YOLO v5, v9, v10, and v11. We achieved improved performance in detecting defects in solar panels compared to existing solutions by using the YOLO v10 and v11 algorithms. Additionally, we compared their performance results against Support Vector Machine (SVM) and Faster Region-based Convolutional Neural Network (R-CNN) to demonstrate the efficiency of the YOLO v10 algorithm. In conclusion, YOLO v11-X delivered the best performance among the algorithms tested. It reached a precision rate, recall rate, mean average precision, and F1 score of about 89.7%, 87.7%, 92.7%, and 90%, respectively.
Perception, Trust, and Motivation in Consumer Behavior for Organic Food Acquisition: An Exploratory Study
Authors: Moroșan Elena , Popovici Violeta , Popescu Ioana Andreea , Daraban Adriana , Karampelas Oana , Matac Liviu Marian , Licu Monica , Rusu Andreea , Chirigiu Larisa-Marina-Elisabeth , Opriţescu Sinziana , Ionita Elena Iuliana , Saulean Alina , Nitescu Maria
Published: January 2025

(1) Background: A sustainable healthy diet assures human well-being in all life stages, protects environmental resources, and preserves biodiversity. This work investigates the sociodemographic factors, knowledge, trust, and motivations involved in organic food acquisition behavior. (2) Methods: An online survey via Google Forms platform, with 316 respondents, was conducted from 1 March to 31 May 2024. (3) Results: Our findings show that suitably informed people with high educational levels (academic and post-college) report significant satisfaction with organic food consumption (p < 0.05). There is also a considerable correlation between ages 25–65, moderate to high satisfaction, and “yes” for eco-food recommendations (p < 0.05). The same satisfaction levels are associated with medium confidence in eco-food labels and a moderate to high monthly income (p < 0.05). Our results show that monthly income and residence are not essential factors in higher price perception. Insignificant price variation perception correlated with high confidence and weekly acquisition (p < 0.05). Similar price perception correlates with the highest confidence level and daily acquisition (p < 0.05). Obese respondents exhibited minimal satisfaction and opted for “abstention” from eco-food recommendations (p < 0.05). (4) Conclusions: The present study extensively analyzed Romanian people’s knowledge, perception, and trust regarding organic foods. It demonstrates that sociodemographic factors differentiate consumers and influence attitudes and motivation for organic food acquisition.
Effects of Shared Decision-Making, Health Literacy, and Self-Care Knowledge on Self-Care Behavior Among Hemodialysis Patients in Korea: A Cross-Sectional Survey
Authors: Lee Hyohjung , Cho Mi-Kyoung
Published: January 2025

Background: Patients undergoing hemodialysis for chronic kidney failure experience various complications and physical and emotional difficulties, leading to decreased quality of life. Self-care behaviors are essential for preventing complications and reducing mortality rates. Effective self-care behaviors significantly depend on shared decision-making, health literacy, and self-care knowledge, each critical in patient self-care performance and disease management. This study aimed to determine the importance and relevance of shared decision-making, health literacy, and self-care knowledge. In particular, it enhances self-care behaviors among hemodialysis patients. Methods: Participants were 108 adult hemodialysis patients from the hemodialysis centers of three medical institutions in Cheongju City, Korea. Moreover, the study utilized a descriptive survey research design. Data were analyzed using descriptive statistics, independent t-tests, one-way ANOVA, Pearson’s correlation coefficients, and multiple linear regression analyses. Results: The average score for self-care behaviors was 125.28 out of 175 points, with high scores for medication and vascular management. Furthermore, there were low scores for social activities and dietary management. Factors influencing self-care behaviors in hemodialysis patients were identified as sex, age, economic status, health literacy, and self-care knowledge. These factors explained 45.2% of the variance in self-care behaviors. Conclusions: To promote self-care behaviors in hemodialysis patients, it is essential to improve health literacy and self-care knowledge, strengthen tailored educational programs, and promote the explanatory role of nurses and shared decision-making. Additionally, comprehensive interventions, including economic support, are necessary.
Towards AI-Assisted Mapmaking: Assessing the Capabilities of GPT-4o in Cartographic Design
Authors: Memduhoğlu Abdulkadir
Published: January 2025

Cartographic design is fundamental to effective mapmaking, requiring adherence to principles such as visual hierarchy, symbolization, and color theory to convey spatial information accurately and intuitively, while Artificial Intelligence (AI) and Large Language Models (LLMs) have transformed various fields, their application in cartographic design remains underexplored. This study assesses the capabilities of a multimodal advanced LLM, GPT-4o, in understanding and suggesting cartographic design elements, focusing on adherence to established cartographic principles. Two assessments were conducted: a text-to-text evaluation and an image-to-text evaluation. In the text-to-text assessment, GPT-4o was presented with 15 queries derived from key concepts in cartography, covering classification, symbolization, visual hierarchy, color theory, and typography. Each query was posed multiple times under different temperature settings to evaluate consistency and variability. In the image-to-text evaluation, GPT-4o analyzed maps containing deliberate cartographic errors to assess its ability to identify issues and suggest improvements. The results indicate that GPT-4o demonstrates general reliability in text-based tasks, with variability influenced by temperature settings. The model showed proficiency in classification and symbolization tasks but occasionally deviated from theoretical expectations. In visual hierarchy and layout, the model performed consistently, suggesting appropriate design choices. In the image-to-text assessment, GPT-4o effectively identified critical design flaws such as inappropriate color schemes, poor contrast and misuse of shape and size variables, offering actionable suggestions for improvement. However, limitations include dependency on input quality and challenges in interpreting nuanced spatial relationships. The study concludes that LLMs like GPT-4o have significant potential in cartographic design, particularly for tasks involving creative exploration and routine design support. Their ability to critique and generate cartographic elements positions them as valuable tools for enhancing human expertise. Further research is recommended to enhance their spatial reasoning capabilities and expand their use of visual variables beyond color, thereby improving their applicability in professional cartographic workflows.
Highlights of Precision Medicine, Genetics, Epigenetics and Artificial Intelligence in Pompe Disease
Authors: Moschetti Marta , Venezia Marika , Giacomarra Miriam , Marsana Emanuela Maria , Zizzo Carmela , Duro Giulia , D’Errico Annalisa , Colomba Paolo , Duro Giovanni
Published: January 2025

Pompe disease is a neuromuscular disorder caused by a deficiency of the enzyme acid alpha-glucosidase (GAA), which leads to lysosomal glycogen accumulation and progressive development of muscle weakness. Two distinct isoforms have been identified. In the infantile form, the weakness is often severe and leads to motor difficulties from the first few months of life. In adult patients, the progression is slower but can still lead to significant loss of mobility. The current inherent difficulties of the disease lie in both early diagnosis and the use of biomarkers. Given that this is a multifactorial disease, a number of components may exert an influence on the disease process; from the degree of pre-ERT (enzyme replacement therapy) muscle damage to the damaged autophagic system and the different pathways involved. What methodology should be employed to study the complex characteristics of Pompe disease? Our approach relies on the application of genetic and epigenetic knowledge, with a progression from proteomics to transcriptomics. It is also becoming increasingly evident that artificial intelligence is a significant area of interest. The objective of this study is to conduct a comprehensive review of the existing literature on the known data and complications associated with the disease in patients with disorders attributed to Pompe disease.
FLAME: Training and Validating a Newly Conceived Model Incorporating Alpha-Glutathione-S-Transferase Serum Levels for Predicting Advanced Hepatic Fibrosis and Acute Cardiovascular Events in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD)
Authors: Dallio Marcello , Romeo Mario , Di Nardo Fiammetta , Vaia Paolo , Napolitano Carmine , Ventriglia Lorenzo , Coppola Annachiara , Silvestrin Alessia , Olivieri Simone , Federico Alessandro
Published: January 2025

Alpha-Glutathione-S-transferase (alphaGST) is a liver enzyme whose serum levels increase with the worsening of fibrosis in alcoholic and viral chronic hepatitis. Its usefulness in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) remains unexplored. From January 2016 to December 2017, 200 patients with MASLD and 30 controls were enrolled. AlphaGST serum levels were measured. Variables related to advanced fibrosis (AF) were selected via Principal Component Analysis (PCA), and the best cut-off (BCO) was estimated using ROC analysis. Liver stiffness measurement (LSM), NAFLD fibrosis (NFS), Fibrosis-4 (FIB-4), and BMI-AST/ALT Ratio-Diabetes (BARD) scores were determined. The first acute cardiovascular events (ACE) in ACE-naïve subjects were recorded over five years. A validation cohort of 60 MASLD patients was enrolled from January 2018 to May 2019 and followed for five years. AlphaGST levels increased with fibrosis stage (p < 0.0001) in both cohorts, showing high accuracy in predicting AF (TrC: AUC 0.89, VlC: AUC 0.89). PCA-selected variables were HbA1c, HDL, and alphaGST, forming the “FLAME” model. FLAME showed superior predictive performance for AF and ACEs compared to other models and scores. FLAME represents a novel tool that accurately predicts AF and ACEs in MASLD.
Covariant Hamilton–Jacobi Formulation of Electrodynamics via Polysymplectic Reduction and Its Relation to the Canonical Hamilton–Jacobi Theory
Authors: Barbachoux Cecile , Pietrzyk Monika E. , Kanatchikov Igor V. , Kholodnyi Valery A. , Kouneiher Joseph
Published: January 2025

The covariant Hamilton–Jacobi formulation of electrodynamics is systematically derived from the first-order (Palatini-like) Lagrangian. This derivation utilizes the De Donder–Weyl covariant Hamiltonian formalism with constraints incroporating generalized Dirac brackets of forms and the associated polysymplectic reduction, which ensure manifest covariance and consistency with the field dynamics. It is also demonstrated that the canonical Hamilton–Jacobi equation in variational derivatives and the Gauss law constraint are derived from the covariant De Donder–Weyl Hamilton–Jacobi formulation after space + time decomposition.
Fuzzy Hilbert Transform of Fuzzy Functions
Authors: Yan Zhibo
Published: January 2025

This paper studies the properties of the Fourier transform of the fuzzy function, and extends the classical Poisson integral formula on the half plane to the fuzzy case, obtaining the composition of the fuzzy set generated by a point in the complex field under the action of the fuzzy function. Further, we define and study the fuzzy Hilbert transform of fuzzy functions and their properties. We prove that when the fuzzy function degenerates to the classical case, the fuzzy Hilbert transform will degenerate to the classical Hilbert transform, which proves that the fuzzy Hilbert transform is an extension of classical transformations in the fuzzy function space. In addition, we point out and prove some properties of the fuzzy Hilbert transform. For some fuzzy functions that meet certain requirements, their fuzzy Hilbert transform is a fuzzy point on 0.