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  1. 121

    Transcriptomic Profiling of Buds Unveils Insights into Floral Initiation in Tea-Oil Tree (<i>Camellia oleifera</i> ‘changlin53’) by Hongyan Guo, Zongshun Zhou, Jian Zhou, Chao Yan, Wenbin Zhong, Chang Li, Ying Jiang, Yaqi Yuan, Linqing Cao, Wenting Pan, Jinfeng Wang, Jia Wang, Tieding He, Yikai Hua, Yisi Liu, Lixian Cao, Chuansong Chen

    Published 2025-07-01
    “…GA<sub>4</sub> was exclusively detected at the sprouting stage (BII), while GA<sub>3</sub> was present in all samples but was significantly lower in BII and the flower bud primordium formation stage (BIII) than in the other samples. …”
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  2. 122
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  4. 124

    A New Disease of Cherry Plum Tree with Yellow Leaf Symptoms Associated with a Novel Phytoplasma in the Aster Yellows Group by Zheng-nan LI, Lei ZHANG, Ye TAO, Ming CHI, Yu XIANG, Yun-feng WU

    Published 2014-08-01
    “…A novel phytoplasma was detected in a cherry plum (Prunus cerasifera Ehrh) tree that mainly showed yellow leaf symptom. …”
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  5. 125
  6. 126

    Prevalence of Quinolones Resistance Proteins Encoding Genes (qnr genes) and Co-Resistance with β-lactams among Klebsiella pneumoniae Isolates from Iraqi Patients by mustafa et al.

    Published 2020-05-01
    “…These isolates were collected from different clinical samples, including 15 (30%) urine, 12 (24%) blood, 9 (18%) sputum, 9 (18%) wound, and 5 (10%) burn. …”
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  7. 127

    Assessing spatial variability of soil macro fauna and tree canopy using fractal theory (Case study: Riparian Forest of Maroon River) by Shaieste Gholami, ehsan sayad, Hamid Taleshi

    Published 2016-08-01
    “…We considered the distance among the samples as 50 m. Tree canopy was measured in 5* 5 plots. …”
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  8. 128

    Phylogenetic analysis and genotypic characterization of coagulase-negative Staphylococcus aureus isolates from sheep subclinical mastitis milk in Nineveh governorate, Iraq by Karam M. Abdulrazzaq, Ayman H, Taha, Omar H. Sheet

    Published 2025-07-01
    “…The current study aimed to isolate and identify coagulase-negative Staphylococcus aureus (CNSA) in non-clinical inflammation of the mammary glands and to detect the nuc, mecA, clfA, clfB, coa, and 16S rRNA genes, along with constructing a phylogenetic tree. …”
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  9. 129
  10. 130

    2-methyl-3-buten-2-ol: A Pheromone Component of Conifer Bark Beetles Found in the Bark of Nonhost Deciduous Trees by Qing-He Zhang, Fredrik Schlyter, Göran Birgersson

    Published 2012-01-01
    “…Only trace amounts of MB were detected in some aeration samples of the fresh bark chips, and no MB was found from the aeration samples of undamaged stems at detectable levels. …”
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  11. 131

    A Novel TKEO With the Decision Tree–Based Method for Fault Analysis of the HVDC Transmission Link Fed by Offshore Wind and Solar Farms by Rajesh Babu Damala, Ramana Pilla, V. Manoj, S. Ramana Kumar Joga, Chidurala Saiprakash, Theophilus A. T. Kambo

    Published 2025-01-01
    “…Detecting and classifying various faults on high voltage DC transmission (HVDC) lines and pinpointing their locations are crucial tasks for the power system’s efficient operation. …”
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  12. 132
  13. 133

    Molecular Detection and Characterization of Haemonchus spp. in Cattle in Nigeria by Olamilekan Banwo, Olalekan Jeremiah, Rofiat Adesina, Abraham Adeyemo, Olusegun Fagbohun

    Published 2024-11-01
    “…Methods: To detect and characterize Haemonchus species in cases of haemonchosis at a Municipal abattoir in Ibadan, Nigeria; abomasal samples were collected from cattle at the abattoir. …”
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  14. 134

    First report of virus detection in Ficus carica in Austria by Eduviges Glenda BORROTO FERNANDEZ, Toufic ELBEAINO, Florian FÜRNSINN, Anna KEUTGEN, Norbert KEUTGEN, Margit LAIMER

    Published 2024-02-01
    “…For future propagation of some fig varieties, the phytosanitary status of eight fig accessions, representing four Austrian genotypes maintained in a varietal collection plot, was investigated using PCR assays for presence of eight fig-infecting viruses. The four fig trees were infected with fig mosaic virus (FMV), fig badnavirus 1 (FBV-1), fig leaf mottle-associated virus 1 (FLMaV-1), fig mild mottle-associated virus (FMMaV) and fig fleck-associated virus (FFkaV); whereas fig leaf mottle-associated virus 2 (FLMaV-2), fig latent virus 1 (FLV-1) and fig cryptic virus 1 (FCV-1) were not detected. …”
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  15. 135

    Comparison of the quality of logistic regression models and a classification tree in predicting hospital mortality in elderly patients with non-ST-elevation myocardial infarction by K. G. Pereverzeva, S. S. Yakushin, N. N. Peregudova, M. V. Mishutina

    Published 2024-10-01
    “…Using the CHAID (Chi Squared Automatic Interaction Detection) method to develop a classification tree for predicting hospital mortality in patients with non-ST-elevation myocardial infarction (non-STEMI) aged 75 years and older and compare the quality of the constructed model with the logistic regression model.Material and methods. …”
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  16. 136

    Comparative study on 16s rRNA gene Sequences for clinical and environmental isolates of legionella pneumophila. by Mohammed Alani, Ibrahim Alani

    Published 2024-06-01
    “…The M1 sample was likewise assumed to have come from the same tree as the other strains, including H1. …”
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    Semi-Supervised Learning for Intrusion Detection in Large Computer Networks by Brandon Williams, Lijun Qian

    Published 2025-05-01
    “…This research explores data-driven security by applying semi-supervised machine learning techniques for intrusion detection in large-scale network environments. Novel methods (including decision tree with entropy-based uncertainty sampling, logistic regression with self-training, and co-training with random forest) are proposed to perform intrusion detection with limited labeled data. …”
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  19. 139

    A Method for Detecting Tomato Maturity Based on Deep Learning by Song Wang, Jianxia Xiang, Daqing Chen, Cong Zhang

    Published 2024-11-01
    “…In addition, by replacing the original CIOU loss function with the Focal–EIOU loss function, the problem of sample imbalance is solved and the detection performance of the model in complex scenarios is improved. …”
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  20. 140

    Developing an Intelligent System for Efficient Botnet Detection in IoT Environment by Ramesh Singh Rawat, Manoj Diwakar, Umang Garg, Prakash Srivastava

    Published 2025-04-01
    “…The device's operation could occasionally be delayed. The sample dataset must be well structured for training the model and validating the suggested model to create the best protection system model feasible for detecting cyber risks. …”
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