bluuebunny/crossref_metadata_2025_split huggingface dataset:
An experimental study of the process of felt understanding in intergroup relations: Japanese and Chinese relations in Japan
Authors: Ioku Tomohiro , Watamura Eiichiro 
Published: June 2024

Abstract“Felt understanding” is a crucial determinant of positive interpersonal and intergroup relationships. However, the question of why felt understanding shapes intergroup relations has been neglected. In a pre-registered test of the process in intergroup relations with a sample from East Asia, we manipulated felt understanding (understood versus misunderstood by an outgroup) in an experimental study (N = 476). The results supported the expectation that felt understanding would lead to a more positive intergroup orientation and action intention. The results of parallel mediation analyses showed that felt understanding indirectly predicted intergroup outcomes through felt positive regard, intergroup overlap, and outgroup stereotypes. Furthermore, the results of post-hoc sequential mediation analyses indicated that felt understanding indirectly predicted intergroup outcomes sequentially through felt positive regard and intergroup overlap, followed by outgroup stereotypes.
Oncolytic adenovirus encoding LHPP exerts potent antitumor effect in lung cancer
Authors: Zhao Yaru , Liu Huihui , Zhan Qi , Jin Hao , Wang Yiqiang , Wang Hui , Huang Biao , Huang Fang , Jia Xiaoyuan , Wang Yigang , Wang Xiaoyan 
Published: June 2024

AbstractLHPP has been shown to be a new tumor suppressor, and has a tendency to be under-expressed in a variety of cancers. Oncolytic virotheray is a promising therapeutics for lung cancer in recent decade years. Here we successfully constructed a new recombinant oncolytic adenovirus GD55-LHPP and investigated the effect of GD55-LHPP on the growth of lung cancer cells in vitro and in vivo. The results showed that LHPP had lower expression in either lung cancer cells or clinical lung cancer tissues compared with normal cells or tissues, and GD55-LHPP effectively mediated LHPP expression in lung cancer cells. GD55-LHPP could effectively inhibit the proliferation of lung cancer cell lines and rarely affected normal cell growth. Mechanically, the oncolytic adenovirus GD55-LHPP was able to induce stronger apoptosis of lung cancer cells compared with GD55 through the activation of caspase signal pathway. Notably, GD55-LHPP also activated autophagy-related signal pathway. Further, GD55-LHPP efficiently inhibited tumor growth in lung cancer xenograft in mice and prolonged animal survival rate compared with the control GD55 or PBS. In conclusion, the novel construct GD55-LHPP provides a valuable strategy for lung cancer-targeted therapy and develop the role of tumor suppress gene LHPP in lung cancer gene therapy.
Application of density clustering with noise combined with particle swarm optimization in UWB indoor positioning
Authors: Guo Hua , Yin Haozhou , Song Shanshan , Zhu Xiuwei , Ren Daokuan 
Published: June 2024

AbstractDue to the presence of non-line-of-sight (NLOS) obstacles, the localization accuracy in ultra-wideband (UWB) wireless indoor localization systems is typically substantially lower. To minimize the influence of these environmental factors and improve the accuracy of indoor wireless positioning, this paper proposes a density clustering with noise combined with particle swarm optimization (DCNPSO) to improve UWB positioning. Which exploits the advantages of the density-based spatial clustering algorithm with noise (DBSCAN) and particle swarm optimization (PSO) algorithm. The experimental results show that the DCNPSO algorithm achieves 45.25% and 36.14% higher average positioning accuracy than the DBSCAN and PSO algorithms, respectively. The positioning error of this algorithm remains stable within 3 cm in static positioning and can achieve high accuracy in NLOS environments.
Characterization of dUTPase expression in mouse postnatal development and adult neurogenesis
Authors: Nagy Nikolett , Hádinger Nóra , Tóth Otília , Rácz Gergely Attila , Pintér Tímea , Gál Zoltán , Urbán Martin , Gócza Elen , Hiripi László , Acsády László , Vértessy Beáta G. 
Published: June 2024

AbstractThe enzyme dUTPase has an essential role in maintaining genomic integrity. In mouse, nuclear and mitochondrial isoforms of the enzyme have been described. Here we present the isoform-specific mRNA expression levels in different murine organs during development using RT-qPCR. In this study, we analyzed organs of 14.5-day embryos and of postnatal 2-, 4-, 10-week- and 13-month-old mice. We demonstrate organ-, sex- and developmental stage-specific differences in the mRNA expression levels of both isoforms. We found high mRNA expression level of the nuclear isoform in the embryo brain, and the expression level remained relatively high in the adult brain as well. This was surprising, since dUTPase is known to play an important role in proliferating cells, and mass production of neural cells is completed by adulthood. Thus, we investigated the pattern of the dUTPase protein expression specifically in the adult brain with immunostaining and found that dUTPase is present in the germinative zones, the subventricular and the subgranular zones, where neurogenesis occurs and in the rostral migratory stream where neuroblasts migrate to the olfactory bulb. These novel findings suggest that dUTPase may have a role in cell differentiation and indicate that accurate dTTP biosynthesis can be vital, especially in neurogenesis.
Estimation of the amount of pear pollen based on flowering stage detection using deep learning
Authors: Endo Keita , Hiraguri Takefumi , Kimura Tomotaka , Shimizu Hiroyuki , Shimada Tomohito , Shibasaki Akane , Suzuki Chisa , Fujinuma Ryota , Takemura Yoshihiro 
Published: June 2024

AbstractPear pollination is performed by artificial pollination because the pollination rate through insect pollination is not stable. Pollen must be collected to secure sufficient pollen for artificial pollination. However, recently, collecting sufficient amounts of pollen in Japan has become difficult, resulting in increased imports from overseas. To solve this problem, improving the efficiency of pollen collection and strengthening the domestic supply and demand system is necessary. In this study, we proposed an Artificial Intelligence (AI)-based method to estimate the amount of pear pollen. The proposed method used a deep learning-based object detection algorithm, You Only Look Once (YOLO), to classify and detect flower shapes in five stages, from bud to flowering, and to estimate the pollen amount. In this study, the performance of the proposed method was discussed by analyzing the accuracy and error of classification for multiple flower varieties. Although this study only discussed the performance of estimating the amount of pollen collected, in the future, we aim to establish a technique for estimating the time of maximum pollen collection using the method proposed in this study.
Stress–strain curve and elastic behavior of the fibrotic lung with usual interstitial pneumonia pattern during protective mechanical ventilation
Authors: Tonelli Roberto , Rizzoni Raffaella , Grasso Salvatore , Cortegiani Andrea , Ball Lorenzo , Samarelli Anna Valeria , Fantini Riccardo , Bruzzi Giulia , Tabbì Luca , Cerri Stefania , Manicardi Linda , Andrisani Dario , Gozzi Filippo , Castaniere Ivana , Smit Marry R. , Paulus Frederique , Bos Lieuwe D. J. , Clini Enrico , Marchioni Alessandro 
Published: June 2024

AbstractPatients with acute exacerbation of lung fibrosis with usual interstitial pneumonia (EUIP) pattern are at increased risk for ventilator-induced lung injury (VILI) and mortality when exposed to mechanical ventilation (MV). Yet, lack of a mechanical model describing UIP-lung deformation during MV represents a research gap. Aim of this study was to develop a constitutive mathematical model for UIP-lung deformation during lung protective MV based on the stress–strain behavior and the specific elastance of patients with EUIP as compared to that of acute respiratory distress syndrome (ARDS) and healthy lung. Partitioned lung and chest wall mechanics were assessed for patients with EUIP and primary ARDS (1:1 matched based on body mass index and PaO2/FiO2 ratio) during a PEEP trial performed within 24 h from intubation. Patient’s stress–strain curve and the lung specific elastance were computed and compared with those of healthy lungs, derived from literature. Respiratory mechanics were used to fit a novel mathematical model of the lung describing mechanical-inflation-induced lung parenchyma deformation, differentiating the contributions of elastin and collagen, the main components of lung extracellular matrix. Five patients with EUIP and 5 matched with primary ARDS were included and analyzed. Global strain was not different at low PEEP between the groups. Overall specific elastance was significantly higher in EUIP as compared to ARDS (28.9 [22.8–33.2] cmH2O versus 11.4 [10.3–14.6] cmH2O, respectively). Compared to ARDS and healthy lung, the stress/strain curve of EUIP showed a steeper increase, crossing the VILI threshold stress risk for strain values greater than 0.55. The contribution of elastin was prevalent at lower strains, while the contribution of collagen was prevalent at large strains. The stress/strain curve for collagen showed an upward shift passing from ARDS and healthy lungs to EUIP lungs. During MV, patients with EUIP showed different respiratory mechanics, stress–strain curve and specific elastance as compared to ARDS patients and healthy subjects and may experience VILI even when protective MV is applied. According to our mathematical model of lung deformation during mechanical inflation, the elastic response of UIP-lung is peculiar and different from ARDS. Our data suggest that patients with EUIP experience VILI with ventilatory setting that are lung-protective for patients with ARDS.
Silicon–calcium fertilizer increased rice yield and quality by improving soil health
Authors: Yuan Shuai , Han Yu , Cui Can , Chen Pingping , Tu Naimei , Rang Zhongwen , Yi Zhenxie 
Published: June 2024

AbstractIt is important to ensure the nutritional quality and safe production of rice. Here, plot experiments were used to analyze the effects of three soil amendments—10 t ha−1 of biochar (BC), 1.5 t ha−1 of lime (LM), and 2.25 t ha−1 of silicon–calcium fertilizer (SC)—on the soil characteristics, rice yield and quality of double-cropping rice grown in mildly cadmium-polluted paddy fields. Compared with the control treatment (CK), the BC and SC treatments significantly improved rice processing, appearance and nutritional quality, but reduced cooking quality. All three soil amendments significantly reduced cadmium (Cd) content in brown rice. Soil amendments could significantly increase soil pH and reduce soil available Cd content. The application of the BC and SC treatments increased the content of each nutrient index in the soil (SOM, NN, AP, AK). Correlation analysis showed that the improvement in rice processing, appearance, and nutritional quality was mainly affected by the comprehensive effects of soil SOM, NN, AP and AK; the hygiene quality was mainly affected by soil pH and available Cd. In terms of benefit analysis combined with cost, the SC treatment had the highest benefit effect. Taken together, in mildly cadmium-polluted paddy fields, the application of silicon–calcium fertilizer improved the soil quality, thereby increased the yield and quality of rice, and had the best effect on increasing income.
Enhancing customer retention in telecom industry with machine learning driven churn prediction
Authors: Sikri Alisha , Jameel Roshan , Idrees Sheikh Mohammad , Kaur Harleen 
Published: June 2024

AbstractCustomer churn remains a critical concern for businesses, highlighting the significance of retaining existing customers over acquiring new ones. Effective prediction of potential churners aids in devising robust retention policies and efficient customer management strategies. This study dives into the realm of machine learning algorithms for predictive analysis in churn prediction, addressing the inherent challenge posed by diverse and imbalanced customer churn data distributions. This paper introduces a novel approach—the Ratio-based data balancing technique, which addresses data skewness as a pre-processing step, ensuring improved accuracy in predictive modelling. This study fills gaps in existing literature by highlighting the effectiveness of ensemble algorithms and the critical role of data balancing techniques in optimizing churn prediction models. While our research contributes a novel approach, there remain avenues for further exploration. This work evaluates several machine learning algorithms—Perceptron, Multi-Layer Perceptron, Naive Bayes, Logistic Regression, K-Nearest Neighbour, Decision Tree, alongside Ensemble techniques such as Gradient Boosting and Extreme Gradient Boosting (XGBoost)—on balanced datasets achieved through our proposed Ratio-based data balancing technique and the commonly used Data Resampling. Results reveal that our proposed Ratio-based data balancing technique notably outperforms traditional Over-Sampling and Under-Sampling methods in churn prediction accuracy. Additionally, using combined algorithms like Gradient Boosting and XGBoost showed better results than using single methods. Our study looked at different aspects like Accuracy, Precision, Recall, and F-Score, finding that these combined methods are better for predicting customer churn. Specifically, when we used a 75:25 ratio with the XGBoost method, we got the most promising results for our analysis which are presented in this work.
An ecological assessment of decision-making under risk and ambiguity through the virtual serious game Kalliste Decision Task
Authors: Molins Francisco , Gil-Gómez José-Antonio , Serrano Miguel Ángel , Mesa-Gresa Patricia 
Published: June 2024

AbstractTraditional methods for evaluating decision-making provide valuable insights yet may fall short in capturing the complexity of this cognitive capacity, often providing insufficient for the multifaceted nature of decisions. The Kalliste Decision Task (KDT) is introduced as a comprehensive, ecologically valid tool aimed at bridging this gap, offering a holistic perspective on decision-making. In our study, 81 participants completed KDT alongside established tasks and questionnaires, including the Mixed Gamble Task (MGT), Iowa Gambling Task (IGT), and Stimulating & Instrumental Risk Questionnaire (S&IRQ). They also completed the User Satisfaction Evaluation Questionnaire (USEQ). The results showed excellent usability, with high USEQ scores, highlighting the user-friendliness of KDT. Importantly, KDT outcomes showed significant correlations with classical decision-making variables, shedding light on participants’ risk attitudes (S&IRQ), rule-based decision-making (MGT), and performance in ambiguous contexts (IGT). Moreover, hierarchical clustering analysis of KDT scores categorized participants into three distinct profiles, revealing significant differences between them on classical measures. The findings highlight KDT as a valuable tool for assessing decision-making, addressing limitations of traditional methods, and offering a comprehensive, ecologically valid approach that aligns with the complexity and heterogeneity of real-world decision-making, advancing research and providing insights for understanding and assessing decision-making across multiple domains.
Genetic diversity and population structure of Physella acuta (Gastropoda: Physidae) in Thailand using mitochondrial gene markers: COI and 16S rDNA
Authors: Dumidae Abdulhakam , Ardpairin Jiranun , Pansri Supawan , Homkaew Chanatinat , Nichitcharoen Mayura , Thanwisai Aunchalee , Vitta Apichat 
Published: June 2024

AbstractPhysella acuta is a freshwater snail native to North America. Understanding the phylogeography and genetic structure of P. acuta will help elucidate its evolution. In this study, we used mitochondrial (COI and 16S rDNA) and nuclear (ITS1) markers to identify the species and examine its genetic diversity, population structure, and demographic history of P. acuta in Thailand. Phylogenetic and network analyses of P. acuta in Thailand pertained to clade A, which exhibits a global distribution. Analysis of the genetic structure of the population revealed that the majority of pairwise comparisons showed no genetic dissimilarity. An isolation-by-distance test indicates no significant correlation between genetic and geographical distances among P. acuta populations, suggesting that gene flow is not restricted by distance. Demographic history and haplotype network analyses suggest a population expansion of P. acuta, as evidenced by the star-like structure detected in the median-joining network. Based on these results, we concluded that P. acuta in Thailand showed gene flow and recent population expansion. Our findings provide fundamental insights into the genetic variation of P. acuta in Thailand.
Global agricultural N2O emission reduction strategies deliver climate benefits with minimal impact on stratospheric O3 recovery
Authors: Weber James , Keeble James , Abraham Nathan Luke , Beerling David J. , Martin Maria Val 
Published: June 2024

AbstractAgricultural nitrous oxide (N2O) emission reduction strategies are required given the potency of N2O as a greenhouse gas. However, the growing influence of N2O on stratospheric ozone (O3) with declining stratospheric chlorine means the wider atmospheric impact of N2O reductions requires investigation. We calculate a N2O emission reduction of 1.35 TgN2O yr-1 (~5% of 2020 emissions) using spatially separate deployment of nitrification inhibitors ($70–113 tCO2e−1) and crushed basalt (no-cost co-benefit) which also sequesters CO2. In Earth System model simulations for 2025–2075 under high (SSP3-7.0) and low (SSP1-2.6) surface warming scenarios, this N2O mitigation reduces NOx-driven O3 destruction, driving regional stratospheric O3 increases but with minimal impact on total O3 column recovery. By 2075, the radiative forcing of the combined N2O and CO2 reductions equates to a beneficial 9–11 ppm CO2 removal. Our results support targeted agricultural N2O emission reductions for helping nations reach net-zero without hindering O3 recovery.
Integration of pre-treatment computational radiomics, deep radiomics, and transcriptomics enhances soft-tissue sarcoma patient prognosis
Authors: Crombé Amandine , Lucchesi Carlo , Bertolo Frédéric , Kind Michèle , Spalato-Ceruso Mariella , Toulmonde Maud , Chaire Vanessa , Michot Audrey , Coindre Jean-Michel , Perret Raul , Le Loarer François , Bourdon Aurélien , Italiano Antoine 
Published: June 2024

AbstractOur objective was to capture subgroups of soft-tissue sarcoma (STS) using handcraft and deep radiomics approaches to understand their relationship with histopathology, gene-expression profiles, and metastatic relapse-free survival (MFS). We included all consecutive adults with newly diagnosed locally advanced STS (N = 225, 120 men, median age: 62 years) managed at our sarcoma reference center between 2008 and 2020, with contrast-enhanced baseline MRI. After MRI postprocessing, segmentation, and reproducibility assessment, 175 handcrafted radiomics features (h-RFs) were calculated. Convolutional autoencoder neural network (CAE) and half-supervised CAE (HSCAE) were trained in repeated cross-validation on representative contrast-enhanced slices to extract 1024 deep radiomics features (d-RFs). Gene-expression levels were calculated following RNA sequencing (RNAseq) of 110 untreated samples from the same cohort. Unsupervised classifications based on h-RFs, CAE, HSCAE, and RNAseq were built. The h-RFs, CAE, and HSCAE grouping were not associated with the transcriptomics groups but with prognostic radiological features known to correlate with lower survivals and higher grade and SARCULATOR groups (a validated prognostic clinical-histological nomogram). HSCAE and h-RF groups were also associated with MFS in multivariable Cox regressions. Combining HSCAE and transcriptomics groups significantly improved the prognostic performances compared to each group alone, according to the concordance index. The combined radiomic-transcriptomic group with worse MFS was characterized by the up-regulation of 707 genes and 292 genesets related to inflammation, hypoxia, apoptosis, and cell differentiation. Overall, subgroups of STS identified on pre-treatment MRI using handcrafted and deep radiomics were associated with meaningful clinical, histological, and radiological characteristics, and could strengthen the prognostic value of transcriptomics signatures.
Automated assessment of cardiac dynamics in aging and dilated cardiomyopathy Drosophila models using machine learning
Authors: Melkani Yash , Pant Aniket , Guo Yiming , Melkani Girish C. 
Published: June 2024

AbstractThe Drosophila model is pivotal in deciphering the pathophysiological underpinnings of various human ailments, notably aging and cardiovascular diseases. Cutting-edge imaging techniques and physiology yield vast high-resolution videos, demanding advanced analysis methods. Our platform leverages deep learning to segment optical microscopy images of Drosophila hearts, enabling the quantification of cardiac parameters in aging and dilated cardiomyopathy (DCM). Validation using experimental datasets confirms the efficacy of our aging model. We employ two innovative approaches deep-learning video classification and machine-learning based on cardiac parameters to predict fly aging, achieving accuracies of 83.3% (AUC 0.90) and 79.1%, (AUC 0.87) respectively. Moreover, we extend our deep-learning methodology to assess cardiac dysfunction associated with the knock-down of oxoglutarate dehydrogenase (OGDH), revealing its potential in studying DCM. This versatile approach promises accelerated cardiac assays for modeling various human diseases in Drosophila and holds promise for application in animal and human cardiac physiology under diverse conditions.
An orange peel-based hydrogel composite for touch-responsive electronic skin
Authors: Ran Zhuoqing , Xu Jie , Zeng Wenyan , Leng Yongping , Wu Bin , Zhan Xueqing , Tsai Fang-Chang , Ma Ning 
Published: June 2024

AbstractAs global environmental issues increase, sustainable use, disposal, and production of materials play an important role. The world produces 1.3 billion tons of food waste annually, of which about 15% are edible fruit peels. Here, we use freeze-dried orange peels as the main material in a hydrogel to prepare a flexible, natural, biocompatible, and sustainably produced electronic skin. We introduce the lyophilized mesocarp of orange and a copper-based metal-organic framework into a polyvinyl alcohol/hyaluronic acid matrix hydrogel, which can occur under mild conditions. The design allows the polymers to connect through intermolecular interactions rather than covalent bonding, which improves the material’s mechanical properties while retaining the self-healing ability. The orange peel-based hydrogel exhibits high elongation at break (290%), enhanced tensile stress, self-healing, conductivity (0.14 S/m), and antibacterial properties (95.3%). These results demonstrate an option for environmentally friendly materials for electronic skin.