bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Role of Nanotubes in Modulating Colony Spreading and Autolysis in Staphylococcus aureus Biofilms
Authors: Ahmed Faraz , Urooj Shaista , Ahmed Ayaz , Hudda Hafiza Noor , Mirani Zulfiqar Ali , Malakar Pradeep K. 
Published: June 2024

Abstract

This study investigated and characterized S. aureus biofilm dynamics. Transmission electron micrographs revealed the presence of connecting nanostructures, here referend to as nanotubes, among cells in close vicinity in S. aureus biofilms. It was further known that the nanotubes had significant implications for colony spreading and autolysis. Cell clumps producing nanotubes exhibited slower growth rates and a lack of colony spreading, suggesting that nanotube formation may hinder the cells' ability to spread and colonize new areas. In contrast, the planktonic population, which lacked significant nanotube formation, displayed faster growth and colony spreading. Furthermore, Triton (T) X100-induced autolysis assays demonstrated that cell clumps and surface-attached cells, which exhibited prominent nanotube structures, were more susceptible to autolysis. In contrast, planktonic cells, which did not show significant nanotube formation, displayed higher resistance to autolysis. These findings suggest that nanotube formation may render cells more vulnerable to autolysis. The presence or absence of nanotubes in different cell populations within the biofilm influenced their ability to spread, colonize, and survive in challenging environments.
Boundary scenario generation method of the autonomous system based on adaptive Poisson disk sampling
Authors: Lu Hui , Wang Xirui , Wang Shiqi 
Published: June 2024

Abstract

Generation of boundary scenario is a common approach used to test and evaluate the autonomous system. It can generate amount number of test cases in regions that demonstrate critical transitions in performance modes. However, getting sufficient test cases within boundaries in a short time remains a huge challenge for a system under test. The choice of sampling method has a great influence on efficiency and accuracy of the test. This paper proposed a new black-box test method based on adaptive Poisson disk sampling to search the test cases in decision boundary of system under test. Firstly, the idea of sequential sampling and optimization are borrowed to guide Poisson disk sampling to sample in the boundary region of the black-box system. In order to improve the convergence speed of the algorithm, an adaptive search strategy based on dichotomous method is adopted. In addition, an elimination criterion is proposed to further decrease the calculation cost and the introduction of invalid samples. Finally, two state-of-art methods were compared on nine different categories of benchmarks and two actual autonomous systems to demonstrate the validation and efficiency of proposed method. The result shows that the proposed method can obtain more information of the system with fewer samples.
Linalool as a Potential Agent for Inhibiting Escherichia coli Biofilm Formation and Exopolysaccharide Production
Authors: Wang Lei , Wang Jiamian , Zhang Kang , Zhang Jingyan , Wang Junyan , Ji Peng , Wei Yanming , Li Jianxi 
Published: June 2024

Abstract

Linalool—a compound commonly found in various plant essential oils—exhibits multiple biological activities, encompassing anti-bacterial, inflammatory, analgesic, and antioxidant properties. However, the linalool implications on Escherichia coli (E. coli) biofilm (BF) remain underexplored. We proposed to ascertain the linalool implications on the development of E. coli BF and its extracellular polysaccharides, as well as to assess the implications of linalool on E. coli in both suspension and BF states. We discovered that the minimum biofilm inhibitory concentrations (MBICs) of linalool versus E. coli were twice as high as the lowest inhibitory concentrations. Linalool had a deadly impact on clinical E. coli strains obtained from cows with clinical endometritis, regardless of whether they were in a planktonic or BF condition. Linalool suppressed the BF development in a way that was dependent on the dosage, with an MBIC of 4 µL/mL. This was verified by the use of the crystal violet test and scanning electron microscopy. Moreover, the CCK-8 assay and confocal laser scanning microscopy (CLSM) manifested significant hindrances in live bacteria within the BF. The concentration of extracellular polymeric compounds in the E. coli BF was also mitigated. Furthermore, CLSM and RT-qPCR analysis confirmed that linalool (2 µL/mL) significantly suppressed exopolysaccharide (EPS) and the pgaABCD gene expression, regulating an essential exopolysaccharide expression in BF formation. These findings revealed that linalool effectively suppressed viable bacteria, EPS production, and E. coli BF formation, presenting a new method for preventing BF-related infections.
An inverter fault diagnosis method based on ISMO and average log-likelihood ratio
Authors: Han Sumin , Zhao Guoshuai , Gao Jingli , Jia Jiaoxin 
Published: June 2024

Abstract

Conventional methods for diagnosing open-circuit faults in three-phase voltage-source inverters (TP-VSI) suffer from limited diagnostic diversity and slow response times. Addressing this issue, this paper proposes a fault diagnosis method based on an improved sliding mode observer (ISMO). Initially, the construction of the SMO is grounded in the mathematical model of TP-VSI. Additionally, the integration of the power switching function into the SMO aims to mitigate jitter. Subsequently, a novel fault feature is extracted, leveraging the average log-likelihood ratio based on the observer-estimated current and the actual current. Finally, a delayed switching adaptive threshold (DSAT) is devised using logical operations. The DSAT aims to fulfill the requirements for diagnosing two IGBT faults while enhancing adaptability to dynamic operating conditions. Experimental results demonstrate that the proposed fault diagnosis method can effectively identify 21 types of open-circuit faults, achieving a diagnosis time of less than 75% of the current cycle. The method exhibits high accuracy and robustness.
AI-driven convolutional neural networks for accurate identification of yellow fever vectors
Authors: Araújo Taís Oliveira , Miranda Vinicius Lima , Gurgel-Gonçalves Rodrigo 
Published: June 2024

Abstract

Background
Identifying mosquito vectors is crucial for controlling diseases. Automated identification studies using Convolutional Neural Network (CNN) have been conducted for some urban mosquito vectors but not yet for sylvatic mosquito vectors that transmit the yellow fever and other arboviruses. Here, we evaluated the ability of the AlexNet CNN to identify four mosquito species: Aedes serratus, Aedes scapularis, Haemagogus leucocelaenus and Sabethes albiprivus and whether there is variation in AlexNet's ability to classify mosquitoes based on pictures of four different body regions.
Methods
The specimens were photographed using a cell phone connected to a stereoscope. Photographs were taken of the full-body, pronotum and lateral view of the thorax, which were pre-processed to train the AlexNet algorithm. The evaluation was based on the confusion matrix, the accuracy (10 pseudoreplicates) and the confidence interval for each experiment.
Results
Our study found that the AlexNet can accurately identify mosquito pictures of the genus Aedes, Sabethes and Haemagogus with over 90% accuracy. Furthermore, the algorithm performance did not change according to the body regions submitted. It is worth noting that the state of preservation of the mosquitoes, which were often damaged, may have affected the network's ability to differentiate between these species and thus accuracy rates could have been even higher.
Conclusions
Our results support the idea of applying CNNs for AI-driven identification of mosquito vectors of tropical diseases. This approach can potentially improve the surveillance of yellow fever vectors by health services and the population as well.
Post-selection inference for quantifying uncertainty in changes in variance
Authors: Carrington Rachel , Fearnhead Paul 
Published: June 2024

Abstract

Quantifying uncertainty in detected changepoints is an important problem. However it is challenging as the naive approach would use the data twice, first to detect the changes, and then to test them. This will bias the test, and can lead to anti-conservative p-values. One approach to avoid this is to use ideas from post-selection inference, which conditions on the information in the data used to choose which changes to test. As a result this produces valid p-values; that is, p-values that have a uniform distribution if there is no change. Currently such methods have been developed for detecting changes in mean only. This paper presents two approaches for constructing post-selection p-values for detecting changes in variance. These vary depending on the method use to detect the changes, but are general in terms of being applicable for a range of change-detection methods and a range of hypotheses that we may wish to test.
Towards robust corridors – a novel validation framework to more efficiently create corridors
Authors: Poor Erin E. , Schieck Brian , Cox John , Guthrie Joe , Mullinax Jennifer M. 
Published: June 2024

Abstract

Context
Ecological corridors are one of the most recommended ways to mitigate biodiversity loss. With growing recognition of corridor importance, robust, quantitative validation methods lag behind the use of corridor modeling.
Objective
We propose a post-hoc corridor validation framework, considering the range of methods across data needs and statistical intensity. We aim to demonstrate the importance of post-hoc corridor validation by testing several validation methods on different corridor model outputs, using the same input data.
Methods
We used three different transformations on a Florida black bear (Ursus americanus floidanus) habitat suitability model to create different resistance grids, independent GPS collar data from a Florida black bear study population, and Circuitscape to create corridor models. We used several validation methods, including a novel method, to compare resulting corridors with management objectives.
Results
Transformed resistance grids were all correlated, yet differing validation and resistance grids resulted in different corridor recommendations. The use of one resistance surface and one validation category can result in the selection of inefficient or ineffective corridors. At a minimum, modelers should determine what proportion of an independent population falls within resulting corridors and should move towards more robust, documented methods as resources allow. The use of multiple validation methods can ensure greater confidence of modeling results.
Conclusions
We encourage the addition and development of the framework presented here to create more robust, effective corridors. If validation methods are not improved, the ecological and economic cost of poor corridor science will continue to increase with increasing biodiversity loss.
The Effect of Whole Body Vibration Exercise Applied at Different Environment Temperatures on Distribution of Irisin Hormone in Muscle Tissue
Authors: OZCAN Nadir Tayfun , DEMIRBAG Emel , BASKURT Zeliha , TONGAL Alper 
Published: June 2024

Abstract

In recent years, the effect of vibration applications on the endocrine system is a subject which has been discussed. Studies have shown that muscle contractions cause some responses in the endocrine system. This study aimed to determine the effects of whole body vibration (WBV) exercise applied at different environmental temperatures on the distribution of the irisin in muscle tissue. In our study, 30 healthy and adult female rats were used. Rats were divided into four groups as a control group (n = 6) and three different experimental groups (n = 8). 1st experimental group at 15 ± 0.5˚C, 2nd experimental group in normal room conditions, 3rd the experimental group at 35 ± 0.5˚C applied WBV exercise 5 sessions a week, 20 minutes in each session for 4 weeks. The control group rats were kept in vibration cassettes at normal room temperature, with the same number and duration of sessions, without WBV exercise. After all experimental procedures were completed, samples of skeletal muscle (M.gastrocnemius) tissue taken from the rats in the groups were used as material. When the localization and distribution of irisin in the skeletal muscle tissue were examined, it was determined that irisin was secreted from both muscle fibers and connective tissue in all groups, and the concentration of irisin synthesized from muscle fiber was the highest in the 3rd experimental group (p < 0.05). As a result, in this study, WBV exercise applied in a hot environment increases the level of irisin in muscle tissue more than WBV exercise applied in a cold environment and at room temperature.
Soil breaking mechanisms increase the efficiency of phosphorus, potassium and water use in soybean under dry season in a paddy field in southern Brazil
Authors: Cunha Italo Machado , Reis Robson Bosa , Silveira Ruan Borges , Sousa Rogério Oliveira , Carlos Filipe , Lima Claudia Liane Rodrigues 
Published: June 2024

Abstract

In the last 20 years there has been a significant expansion of soybean cultivation in the southern Brazil, reaching almost 2 million ha. A significant amount of this area (~500 thousand ha) has been cultivated in lowlands in rotation with rice. This rotation allows for the reduction of weeds and higher nitrogen availability. However, as rice cultivation is by flooding causes numerous physical changes in the soil that impose limitations on development of rainfed crops. In addition to these limitations, soils in the subtropical regions of southern Brazil have low levels of phosphorus and potassium. The aim of this study was to assess the response of the soybean to different soil-breaking mechanisms and doses of phosphorus and potassium in the Southern Brazil. The treatments consisted of using a subsoiler, scarifier and harrow under doses of phosphorus and potassium in the 2020/21 and 2021/22 crop seasons. In the second crop season under water restriction conditions, the scarifier and the subsoiler increased the soybean grain yield by 46% and 28%, respectively. In the same crop season, on average for the scarifier and subsoiler, an increase in the efficiency of phosphorus use (+ 62%), potassium (+ 20%) and water use (+ 47%) was observed in relation to harrow management. With adequate distribution and volume of rainfall, the breaking mechanism did not influence grain yield in soybean. The response of the soybean in terms of grain yield to phosphorus was higher than that of potassium in Entisol in the average of the two crop seasons. Thus, the use of breaking mechanisms is an important strategy for the adequate development of soybean in rotation with irrigated rice in lowlands.
Lump solution, lump and soliton interaction solution, breathing solution, and interference wave solution for the (3+1)-dimensional fourth-order nonlinear equation by bilinear neural network method
Authors: Chen Bohan , Ma Zhimin , Liu Yuanlin , Bi Quanming 
Published: June 2024

Abstract

In this article, the bilinear neural network method(BNNM) is used for the first time to investigate the (3+1)-dimensional fourth-order nonlinear equation. Various bilinear neural network models are created by building diverse layers of neural networks. The precise analytical solutions of this equation consists of lump solution, lump and soliton interaction solution, breathing solution, and interference wave solution. To better reveal the dynamic behavior and physical characteristics of these solutions, constructed 3D maps, contour maps, and density maps. The method adopted in this article helps study nonlinear partial differential equations in nonlinear dynamics, oceanography, and fluid mechanics. Finally, we hope these solutions we obtained can explain some nonlinear phenomena of high-order equations.
A Novel Preparation Approach for Supervised ML Membership Determination of Open Clusters
Authors: Rahimpour Omid , Salek Mohsen , Khakian Mehdi 
Published: June 2024

Abstract

Machine Learning methods have emerged as powerful tools for analyzing stellar clusters, which pose significant challenges. techniques such as DBSCAN and GMM have advanced remarkably in this domain. However, these clustering techniques exhibit imperfections and limitations, highlighting the need for careful data tuning and consideration of data characteristics to ensure meaningful result.
The utilization of supervised Machine Learning techniques for membership determination of the stellar clusters, especially open clusters, can lead to more accurate results. However, the absence of dataset for training on an open cluster presents a significant hurdle. To address the problem, we've introduced a novel approach to generate a labeled dataset for training the supervised Machine Learning models. Our approach leverages data from Gaia DR3 Catalog, which provides precise astrometric and photometric measurements for millions stars in Milky Way, to construct a comprehensive dataset.
Our findings have significant implications for future astronomical research. By using Supervised machine learning techniques, we can achieve more accurate and efficient membership determination for stellar clusters, which can lead to a better understanding of the formation and evolution of galaxies. Our method not only enhances the accuracy of membership determination but also provides insights into the underlying data characteristics that influence cluster analysis.
Bayesian Rank Likelihood Estimation for Spatial Latent Trait Model
Authors: Bekalo Daniel Biftu , Wanjoya Anthony Kibira , Mwalili Samuel Musili 
Published: June 2024

Abstract

In this study, a spatial latent trait model was developed to address the challenge of parameter estimation for ordinal response variables. The development of the model involved employing the Bayesian rank likelihood estimation method. The simulation algorithm was provided in detail, and the performance and sensitivity of the developed method were evaluated using simulation techniques. Method evaluation was conducted to identify any convergence issues in the developed method. The results showed that trace plots of all parameters (β, υ, and γ) showed good mixing and quick convergence. The potential scale reduction factor value for all parameters did not exceed one, indicating that convergence issues were not identified. Additionally, the developed method performed well, as demonstrated by the posterior predictive check, since simulated data generated from the posterior predictive distribution closely resemble the observed data. The developed method also effectively captures within-region variations and spatial correlations between the regions through the latent traits parameters. The assessment of performance included metrics such as root mean square error, mean absolute error, and the probability coverage of the corresponding 95% confidence intervals of the estimates. The results indicate that the estimates obtained from the developed method outperform the existing classical estimates. As a result, it can be concluded that the spatial latent trait model using Bayesian rank likelihood estimation is regarded as the better model.
Validation of the abridged version of the autism-spectrum quotient (AQ-28) in the Arabic-speaking adult general population
Authors: Fekih-Romdhane Feten , Sakr Fouad , Alhuwailah Amthal , Chaibi Leila Sarra , Helmy Mai , Shuwiekh Hanaa Ahmed Mohamed , Boudouda Nedjem Eddine , Zarrouq Btissame , Naser Abdallah Y , Jebreen Kamel , Roubi Mohammed Lakhdar , Hassan Bassam Abdul Rasool , Merdad Nisma , Amin Rizwana , Nawajah Inad , Mohammed Ali Haider , Farhan Sinan Subhi , AlAni Omar Abdulwahid , Cheour Majda , Dabbous Mariam , Malaeb Diana , Obeid Sahar , Hallit Souheil 
Published: June 2024

Abstract

Background: The field of autism research in adults has not yet emerged in Arab countries, which is in part due to the lack of valid, reliable autism measures. The objectives of this study were to assess the psychometric properties of the abridged version of the autism-spectrum quotient (AQ-28) in a sample of non-clinical Arabic-speaking adults.
Methods: Adults aged over 18 years from the general population of Lebanon (N=1076, mean age of 27.90±11.81 years, 63.8% females) were recruited online, using the snowball sampling technique.
Results: In terms of factorial validity, the original five-factor model of the AQ-28 was replicate with an acceptable internal consistency. Measurement invariance was established across sex. Mean sum scores observed in male participants were significantly higher than those displayed by females. Autistic traits as measured by the Arabic AQ-28 correlated negatively with secure attachment and positively with loneliness, depression and anxiety.
Conclusion: This study makes a major contribution to the international literature on autism by validating, for the first time, the AQ-28 into Arabic. It is our hope that this scale will help to galvanize efforts to better understand the nature and etiology of the broader autism phenotype in the general population across cultures.
A Systematic Review of Technology-Driven Interest-Based Learning Environments
Authors: Kulkarni Abhishek , Chu Sharon Lynn 
Published: June 2024

Abstract

Personalized learning has been rising to the forefront in the past few decades. One promising form of personalized learning is Interest-Based Learning or IBL. IBL leverages a learner’s interests and integrates them into traditional learning to create relevance with the learning content. It seeks to positively impact learning-related variables such as motivation, attention, and self-efficacy. This approach, although explored in specific applications and contexts, lacks a holistic review of its development over the years, particularly when dealing with its manifestation through technology. This systematic review aims to fill in some of the critical gaps by analyzing: (i) the conceptualization of interest within IBL; (ii) the implementation of IBL strategies; and (iii) the integration of technologies in technology-driven IBL environments. Based on a review of the literature, existing problems and challenges for technology-driven IBL are identified, along with potential research gaps and future directions.
Experiments on flow velocity profiles in mountain river channels
Authors: Peng Jia , Chen Dong , Liang Shizhengxiong , Tang Rongcai , Hu Hong , Wang Hang 
Published: June 2024

ABSTRACT

Research on the vertical profiles of flow velocity in mountainous river channels is limited, particularly in scenarios where complex bed geometries are absent. Due to the coarse roughness and seepage flow on streambeds composed of gravel, the conventional formulae for flow velocity profiles derived from fluvial river channels do not apply to mountainous river channels. Based on flume experiments with a bed packed with natural gravel and a slope ranging from 0.006 to 0.16, we derived a theoretical formula for flow velocity profiles. This new formula integrates the influence of the subsurface flow and velocity reduction near the water surface, demonstrating a strong alignment with measurements. Our findings indicate that for shallow water flow over rough bed surfaces, the turbulence intensity diminishes along the vertical direction in the near-bed region while remaining relatively constant in the upper water body. Contrary to conventional theories which attribute the increase in flow resistance and the decrease in sediment transport rates in mountainous river channels to form drag, our study emphasizes that the subsurface flow plays a significant role in the overall flow resistance of mountainous river channels and should not be overlooked.