bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhancing prostate cancer diagnosis and reducing unnecessary biopsies with [18F]DCFPyL PET/CT imaging in PI-RADS 3/4 patients
Authors: Fu Yang , Zhao Min , Chen Jie , Wen Qiang , Chen Bin 
Published: July 2024

AbstractFor patients presenting with prostate imaging reporting and data system (PI-RADS) 3/4 findings on magnetic resonance imaging (MRI) examinations, the standard recommendation typically involves undergoing a biopsy for pathological assessment to ascertain the nature of the lesion. This course of action, though essential for accurate diagnosis, invariably amplifies the psychological distress experienced by patients and introduces a host of potential complications associated with the biopsy procedure. However, [18F]DCFPyL PET/CT imaging emerges as a promising alternative, demonstrating considerable diagnostic efficacy in discerning benign prostate lesions from malignant ones. This study aims to explore the diagnostic value of [18F]DCFPyL PET/CT imaging for prostate cancer in patients with PI-RADS 3/4 lesions, assisting in clinical decision-making to avoid unnecessary biopsies. 30 patients diagnosed with PI-RADS 3/4 lesions through mpMRI underwent [18F]DCFPyL PET/CT imaging, with final biopsy pathology results as the “reference standard”. Diagnostic performance was assessed through receiver operating characteristic (ROC) analysis, evaluating the diagnostic efficacy of molecular imaging PSMA (miPSMA) visual analysis and semi-quantitative analysis in [18F]DCFPyL PET/CT imaging. Lesions were assigned miPSMA scores according to the prostate cancer molecular imaging standardized evaluation criteria. Among the 30 patients, 13 were pathologically confirmed to have prostate cancer. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of visual analysis in [18F]DCFPyL PET/CT imaging for diagnosing PI-RADS 3/4 lesions were 61.5%, 88.2%, 80.0%, 75.0%, and 76.5%, respectively. Using SUVmax 4.17 as the optimal threshold, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for diagnosis were 92.3%, 88.2%, 85.7%, 93.8%, and 90.0%, respectively. The area under the ROC curve (AUC) for semi-quantitative analysis was 0.94, significantly higher than visual analysis at 0.80. [18F]DCFPyL PET/CT imaging accurately diagnosed benign lesions in 15 (50%) of the PI-RADS 3/4 patients. For patients with PI-RADS 4 lesions, the positive predictive value of [18F]DCFPyL PET/CT imaging reached 100%. [18F]DCFPyL PET/CT imaging provides potential preoperative prediction of lesion nature in mpMRI PI-RADS 3/4 patients, which may aid in treatment decision-making and reducing unnecessary biopsies.
An enhanced jellyfish search optimizer for stochastic energy management of multi-microgrids with wind turbines, biomass and PV generation systems considering uncertainty
Authors: Ahmed Deyaa , Ebeed Mohamed , Kamel Salah , Nasrat Loai , Ali Abdelfatah , Shaaban Mostafa F. , Hussien Abdelazim G. 
Published: July 2024

AbstractThe energy management (EM) solution of the multi-microgrids (MMGs) is a crucial task to provide more flexibility, reliability, and economic benefits. However, the energy management (EM) of the MMGs became a complex and strenuous task with high penetration of renewable energy resources due to the stochastic nature of these resources along with the load fluctuations. In this regard, this paper aims to solve the EM problem of the MMGs with the optimal inclusion of photovoltaic (PV) systems, wind turbines (WTs), and biomass systems. In this regard, this paper proposed an enhanced Jellyfish Search Optimizer (EJSO) for solving the EM of MMGs for the 85-bus MMGS system to minimize the total cost, and the system performance improvement concurrently. The proposed algorithm is based on the Weibull Flight Motion (WFM) and the Fitness Distance Balance (FDB) mechanisms to tackle the stagnation problem of the conventional JSO technique. The performance of the EJSO is tested on standard and CEC 2019 benchmark functions and the obtained results are compared to optimization techniques. As per the obtained results, EJSO is a powerful method for solving the EM compared to other optimization method like Sand Cat Swarm Optimization (SCSO), Dandelion Optimizer (DO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and the standard Jellyfish Search Optimizer (JSO). The obtained results reveal that the EM solution by the suggested EJSO can reduce the cost by 44.75% while the system voltage profile and stability are enhanced by 40.8% and 10.56%, respectively.
Body size and sequence of host colonisation predict the presence of acoustic signalling in beetles
Authors: Bedoya Carol L. , Brockerhoff Eckehard G. , Kirkendall Lawrence R. , Hofstetter Richard W. , Nelson Ximena J. 
Published: July 2024

AbstractAcoustic communication is widespread in beetles, is often sexually dimorphic, and plays a significant role in behaviours such as premating recognition, courtship, and copulation. However, the factors that determine the presence or absence of acoustic signalling in a given species remain unclear. We examined acoustic communication in bark beetles (Scolytinae) and pinhole borers (Platypodinae), which are two speciose groups with widespread sound production capabilities. We show that body size along with the sequence of host colonisation predict the presence of acoustic communication, and report, for the first time in the animal kingdom, a size limit—1.9 mm—below which acoustic signalling ceases to be present.
A coupled model between circadian, cell-cycle, and redox rhythms reveals their regulation of oxidative stress
Authors: Masuda Kosaku , Sakurai Takeshi , Hirano Arisa 
Published: July 2024

AbstractMost organisms possess three biological oscillators, circadian clock, cell cycle, and redox rhythm, which are autonomous but interact each other. However, whether their interactions and autonomy are beneficial for organisms remains unclear. Here, we modeled a coupled oscillator system where each oscillator affected the phase of the other oscillators. We found that multiple types of coupling prevent a high H2O2 level in cells at M phase. Consequently, we hypothesized a high H2O2 sensitivity at the M phase and found that moderate coupling reduced cell damage due to oxidative stress by generating appropriate phase relationships between three rhythms, whereas strong coupling resulted in an elevated cell damage by increasing the average H2O2 level and disrupted the cell cycle. Furthermore, the multicellularity model revealed that phase variations among cells confer flexibility in synchronization with environments at the expense of adaptability to the optimal environment. Thus, both autonomy and synchrony among the oscillators are important for coordinating their phase relationships to minimize oxidative stress, and couplings balance them depending on environments.
Multi-strategy cooperative scheduling for airport specialized vehicles based on digital twins
Authors: Luo Qian , Liu Huaiming , Liu Chang , Deng Qiangqiang 
Published: July 2024

AbstractEfficient specialized vehicle cooperative scheduling is significant for airport operations, particularly during times of high traffic, which reduces the risk of flight delays and increases customer satisfaction. In this paper,we construct a multi-type vehicles collaborative scheduling model with the objectives of minimizing vehicle travel distance and vehicle waiting time. Additionally, a three-layer genetic algorithm is designed, and the crossover and mutation operations are enhanced to address the scheduling model. Due to the numerous uncertainties and stochastic interferences in airport operations, conventional scheduling methods unable to effectively address these challenges, this paper combines improved genetic algorithm, simulation algorithm, and digital twins technology, proposing a multi-strategy scheduling framework for specialized vehicles based on digital twins. The scheduling framework utilises digital twins to capture dynamic data from the airport and continuously adjusts the scheduling plan through the scheduling strategy to ensure robust scheduling for specialized vehicles. In the event of severe delays at the airport, fast and efficient re-scheduling can be achieved. Finally, the effectiveness of the proposed scheduling framework is validated using domestic flight data, and extensive experiments and analyses are conducted in different scenarios. This research contributes to addressing the optimization problem of cooperative scheduling for multi-type vehicles at airports.
Study of alginate-encapsulated phycoerythrin in promoting the biological activity of synbiotic ice cream with Lactobacillus casei
Authors: Chamari Maryam , Anvar Seyed Amir Ali , Pourahmad Rezvan , Nowruzi Bahareh , yousefi Shima 
Published: July 2024

AbstractThis study examines the effect of phycoerythrin (PE) from a cyanobacterial Nostoc strain encapsulated with alginate as a potential prebiotic to produce synbiotic ice cream products with Lactobacillus casei. It was found that the addition of the encapsulated PE affected, mostly favourably, the physicochemical properties, antioxidant activity, probiotic survival, volatile compound contents, and sensory acceptability of the synbiotic ice cream samples before and after aging at the freezing periods of one day to eight weeks. Thus, it confirms the prebiotic potential of PE for synbiotic ice creams with L. casei.
Four dimensional-scanning transmission electron microscopy study on relationship between crystallographic orientation and spontaneous polarization in epitaxial BiFeO3
Authors: Bae In-Tae , Foran Brendan , Paik Hanjong 
Published: July 2024

AbstractSpontaneous polarization and crystallographic orientations within ferroelectric domains are investigated using an epitaxially grown BiFeO3 thin film under bi-axial tensile strain. Four dimensional-scanning transmission electron microscopy (4D-STEM) and atomic resolution STEM techniques revealed that the tensile strain applied is not enough to cause breakdown of equilibrium BiFeO3 symmetry (rhombohedral with space group: R3c). 4D-STEM data exhibit two types of BiFeO3 ferroelectric domains: one with projected polarization vector possessing out-of-plane component only, and the other with that consisting of both in-plane and out-of-plane components. For domains with only out-of-plane polarization, convergent beam electron diffraction (CBED) patterns exhibit “extra” Bragg’s reflections (compared to CBED of cubic-perovskite) that indicate rhombohedral symmetry. In addition, beam damage effects on ferroelectric property measurements were investigated by systematically changing electron energy from 60 to 300 keV.
Assessing the reactions of tourist markets to reinstated travel restrictions in the destination during the post-COVID-19 phase
Authors: Ma Xuankai , Ma Rongxi , Ma Zijing , Wang Jingzhe , Yang Zhaoping , Wang Cuirong , Han Fang 
Published: July 2024

AbstractThis study, leveraging search engine data, investigates the dynamics of China's domestic tourism markets in response to the August 2022 epidemic outbreak in Xinjiang. It focuses on understanding the reaction mechanisms of tourist-origin markets during destination crises in the post-pandemic phase. Notably, the research identifies a continuous rise in the potential tourism demand from tourist origin cities, despite the challenges posed by the epidemic. Further analysis uncovers a regional disparity in the growth of tourism demand, primarily influenced by the economic stratification of origin markets. Additionally, the study examines key tourism attractions such as Duku Road, highlighting its resilient competitive system, which consists of distinctive tourism experiences, economically robust tourist origins, diverse tourist markets, and spatial pattern stability driven by economic factors in source cities, illustrating an adaptive response to external challenges such as crises. The findings provide new insights into the dynamics of tourism demand, offering a foundation for developing strategies to bolster destination resilience and competitiveness in times of health crises.
Evaluating the performance of multilingual models in answer extraction and question generation
Authors: Moreno-Cediel Antonio , del-Hoyo-Gabaldon Jesus-Angel , Garcia-Lopez Eva , Garcia-Cabot Antonio , de-Fitero-Dominguez David 
Published: July 2024

AbstractMultiple-choice test generation is one of the most complex NLP problems, especially in languages other than English, where there is a lack of prior research. After a review of the literature, it has been verified that some methods like the usage of rule-based systems or primitive neural networks have led to the application of a recent architecture, the Transformer architecture, in the tasks of Answer Extraction (AE) and Question Generation (QG). Thereby, this study is centred in searching and developing better models for the AE and QG tasks in Spanish, using an answer-aware methodology. For this purpose, three multilingual models (mT5-base, mT0-base and BLOOMZ-560 M) have been fine-tuned using three different datasets: a translation to Spanish of the SQuAD dataset; SQAC, which is a dataset in Spanish; and their union (SQuAD + SQAC), which shows slightly better results. Regarding the models, the performance of mT5-base has been compared with that found in two newer models, mT0-base and BLOOMZ-560 M. These models were fine-tuned for multiple tasks in literature, including AE and QG, but, in general, the best results are obtained from the mT5 models trained in our study with the SQuAD + SQAC dataset. Nonetheless, some other good results are obtained from mT5 models trained only with the SQAC dataset. For their evaluation, the widely used BLEU1-4, METEOR and ROUGE-L metrics have been obtained, where mT5 outperforms some similar research works. Besides, CIDEr, SARI, GLEU, WER and the cosine similarity metrics have been calculated to present a benchmark within the AE and QG problems for future work.
PND-Net: plant nutrition deficiency and disease classification using graph convolutional network
Authors: Bera Asish , Bhattacharjee Debotosh , Krejcar Ondrej 
Published: July 2024

AbstractCrop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. Hence, continuous health monitoring of plant is very crucial for handling plant stress. The deep learning methods have proven its superior performances in the automated detection of plant diseases and nutrition deficiencies from visual symptoms in leaves. This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN), added upon a base convolutional neural network (CNN). Sometimes, a global feature descriptor might fail to capture the vital region of a diseased leaf, which causes inaccurate classification of disease. To address this issue, regional feature learning is crucial for a holistic feature aggregation. In this work, region-based feature summarization at multi-scales is explored using spatial pyramidal pooling for discriminative feature representation. Furthermore, a GCN is developed to capacitate learning of finer details for classifying plant diseases and insufficiency of nutrients. The proposed method, calledPlantNutrition Deficiency andDiseaseNetwork (PND-Net), has been evaluated on two public datasets for nutrition deficiency, and two for disease classification using four backbone CNNs. The best classification performances of the proposed PND-Net are as follows: (a) 90.00% Banana and 90.54% Coffee nutrition deficiency; and (b) 96.18% Potato diseases and 84.30% on PlantDoc datasets using Xception backbone. Furthermore, additional experiments have been carried out for generalization, and the proposed method has achieved state-of-the-art performances on two public datasets, namely the Breast Cancer Histopathology Image Classification (BreakHis 40$$\times $$×: 95.50%, and BreakHis 100$$\times $$×: 96.79% accuracy) and Single cells in Pap smear images for cervical cancer classification (SIPaKMeD: 99.18% accuracy). Also, the proposed method has been evaluated using five-fold cross validation and achieved improved performances on these datasets. Clearly, the proposed PND-Net effectively boosts the performances of automated health analysis of various plants in real and intricate field environments, implying PND-Net’s aptness for agricultural growth as well as human cancer classification.
Effect of fly ash and curing temperature on the properties of magnesium phosphate repair mortar
Authors: Liu Junxia , Zhang Jingyu , Li Anbang , Xia Xiaomin , Chen Junpeng 
Published: July 2024

AbstractThis article is aimed at discussing the combined effect of mineral admixture and servicing temperature, especially in cold environment, on the properties of magnesium phosphate repair mortar (MPM). The influence mechanism of fly ash content on the microstructure and performance of MPM were firstly investigated, and then the evolution rules in properties of fly ash modified MPM cured at − 20 °C, 0 °C, 20 °C and 40 °C were further revealed. The results show that the incorporation of fly ash has no significant effect on the setting time and fluidity of MPM. When MPM is modified with 10 wt% and 15 wt% fly ash, its mechanical properties, adhesive strength, water resistance, and volume stability are effectively improved. Fly ash reduces the crystallinity and continuity of struvite enriched in hardened MPM, and its particles are embedded among struvite and unreacted MgO. The compressive strength of MPM-10 cured for various ages increases with the elevating of curing temperature, while the flexural strength, interfacial bonding strength, strength retention and linear shrinkage exhibits the opposite laws. When cured at 0 °C and − 20 °C, MPM-10 still has good early strength, water resistance and interfacial bonding properties, which indicates that MPM-10 provides with an ability of emergency repair of cracked components served in cold environments.
A semantic feature enhanced YOLOv5-based network for polyp detection from colonoscopy images
Authors: Wan Jing-Jing , Zhu Peng-Cheng , Chen Bo-Lun , Yu Yong-Tao 
Published: July 2024

AbstractColorectal cancer (CRC) is a common digestive system tumor with high morbidity and mortality worldwide. At present, the use of computer-assisted colonoscopy technology to detect polyps is relatively mature, but it still faces some challenges, such as missed or false detection of polyps. Therefore, how to improve the detection rate of polyps more accurately is the key to colonoscopy. To solve this problem, this paper proposes an improved YOLOv5-based cancer polyp detection method for colorectal cancer. The method is designed with a new structure called P-C3 incorporated into the backbone and neck network of the model to enhance the expression of features. In addition, a contextual feature augmentation module was introduced to the bottom of the backbone network to increase the receptive field for multi-scale feature information and to focus on polyp features by coordinate attention mechanism. The experimental results show that compared with some traditional target detection algorithms, the model proposed in this paper has significant advantages for the detection accuracy of polyp, especially in the recall rate, which largely solves the problem of missed detection of polyps. This study will contribute to improve the polyp/adenoma detection rate of endoscopists in the process of colonoscopy, and also has important significance for the development of clinical work.
Anticancer efficacy of biosynthesized silver nanoparticles loaded with recombinant truncated parasporin-2 protein
Authors: Srisaisap Monrudee , Boonserm Panadda 
Published: July 2024

AbstractBacterial toxins have received a great deal of attention in the development of cancer treatments. Parasporin-2 (PS2Aa1 or Mpp46Aa1) is a Bacillus thuringiensis parasporal protein that preferentially destroys human cancer cells while not harming normal cells, making it a promising anticancer treatment. With the efficient development and sustainable silver nanoparticles (AgNPs) synthesis technology, the biomedical use of AgNPs has expanded. This study presents the development of a novel nanotoxin composed of biosynthesized silver nanoparticles loaded with the N-terminal truncated PS2Aa1 toxin. MOEAgNPs were synthesized using a biological method, with Moringa oleifera leaf extract and maltose serving as reducing and capping agents. The phytochemicals present in M. oleifera leaf extract were identified by GC–MS analysis. MOEAgNPs were loaded with N-terminal truncated PS2Aa1 fused with maltose-binding protein (MBP-tPS2) to formulate PS2-MOEAgNPs. The PS2-MOEAgNPs were evaluated for size, stability, toxin loading efficacy, and cytotoxicity. PS2-MOEAgNPs demonstrated dose-dependent cytotoxicity against the T-cell leukemia MOLT-4 and Jurkat cell lines but had little effect on the Hs68 fibroblast or normal cell line. Altogether, the current study provides robust evidence that PS2-MOEAgNPs can efficiently inhibit the proliferation of T-cell leukemia cells, thereby suggesting their potential as an alternative to traditional anticancer treatments.
Superiority of the triglyceride glucose index over the homeostasis model in predicting metabolic syndrome based on NHANES data analysis
Authors: Wan Haiyan , Cao Hongyi , Ning Peng 
Published: July 2024

AbstractThe triglyceride-glucose (TyG) index is a simple and inexpensive new marker of insulin resistance that is being increasingly used for the clinical prediction of metabolic syndrome (MetS). Nevertheless, there are only a few comparative studies on its predictive capacity for MetS versus those using the traditional homeostasis model assessment (HOMA). We conducted a cross-sectional study using a database from the National Health and Nutrition Examination Survey (1999 March to 2020 pre-pandemic period). Using statistical methods, we compared the predictive abilities of the TyG index and HOMA (including HOMA of insulin resistance [HOMA-IR] and HOMA of beta-cell function [HOMA-β]) for MetS. A total of 34,195 participants were enrolled and divided into the MetS group (23.1%) or no MetS group (76.9%) according to the International Diabetes Federation (IDF) diagnostic criteria. After applying weighted data, the baseline characteristics of the population were described. Following the exclusion of medication influences, the final count was 31,304 participants. Receiver operating characteristic curve analysis revealed that while distinguishing between MetS and no MetS, the TyG index had an area under the curve (AUC) of 0.827 (sensitivity = 71.9%, specificity = 80.5%), and the cutoff was 8.75, slightly outperforming HOMA-IR (AUC = 0.784) and HOMA-β (AUC = 0.614) with a significance of P < 0.01. The prevalence of MetS in the total population calculated using the TyG index cutoff value was 30.9%, which was higher than that reported in the IDF diagnostic criteria. Weighted data analysis using univariate and multivariate logistic regression displayed an independent association between elevated TyG and HOMA-IR with the risk of MetS. Subgroup analysis further revealed differences in the predictive ability of the TyG index among adult populations across various genders and ethnicities, whereas such differences were not observed for children and adolescents. The TyG index is slightly better than HOMA in predicting MetS and may identify more patients with MetS; thus, its applications in a clinical setting can be appropriately increased.