Morphological Characteristics and Molecular Marker-Assisted Identification of Ovary Glabrous Phenotype in the Population of Nanchuan Dachashu (<i>Camellia nanchuanica</i>) by Zhijun Wu, Weifeng Tang, Meng Lei

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Fine mapping of Brebm6, a gene conferring the early-bolting phenotype in Chinese cabbage (Brassica rapa ssp. pekinensis) by Wei Fu, Shengnan Huang, Zhiyong Liu, Yue Gao, Meidi Zhang, Nan Wang, Gaoyang Qu, Hui Feng

“…In mutant ebm6, the causal gene BraA02g003340.3C (BrFLC2) was predicted using MutMap and Kompetitive Allele Specific PCR genotyping (KASP), as the gene encodes a MADS-box transcription factor. …”
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The NuA4/TIP60 histone-modifying complex and Hr78 modulate the Lobe2 mutant eye phenotype by Stonbraker Madison P., DePaul Dominic A., Heidel-Roberts Ethan C., Greene Hallee, Johnson Matthew Logan

“…An Hr78 mutation predicted to uncouple the Jazf-1 interaction but still capable of interactions with transcriptional activators further enhanced the Lobe 2 mutant phenotype, suggesting the loss of a repressing complex. …”
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A Pilot Study on Single-Cell Raman Spectroscopy Combined with Machine Learning for Phenotypic Characterization of <i>Staphylococcus aureus</i> by Li Liu, Junjing Xue, Yang Song, Taijie Zhan, Yang Liu, Xiaohui Song, Li Mei, Duochun Wang, Yu Vincent Fu, Qiang Wei

“…We constructed a single-cell Raman spectral database encompassing 6240 spectra from 10 strains of <i>S. aureus</i> with diverse phenotypic traits and developed a convolutional neural network (CNN) to predict these phenotypes from the Raman spectra. …”
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Diagnostic Challenge of Phenotypic Variability in COL2A1-related Disorders: Four Novel Variants That Expand the Clinical Spectrum by Burcu Yeter, Yasemin Kendir Demirkol, Metin Eser, Ahmet Hamdi Akgülle, Betül Sözeri, Heves Kırmızıbekmez

“…Our goal is to describe the clinical, radiological, and molecular findings of patients with COL2A1-related dysplasia and investigate the phenotype-genotype correlation. We also highlight the challenge of categorizing COL2A1-related diseases with similar clinical and radiological phenotypes. …”
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Case Report: GATA2 deficiency in two families with novel frameshift variants highlighting phenotypic diversity and need for early diagnosis by Miko Morimoto, Takuro Nishikawa, Atsushi Hijikata, Hiroshi Kasabata, Nobuhisa Maeda, Shuji Kanmura, Shogo Horikawa, Jun Nagahama, Aki Nakamura, Tatsuro Nakamura, Takanari Abematsu, Shunsuke Nakagawa, Kazuhiro Shimura, Satoshi Narumi, Hirokazu Kanegane, Yasuhiro Okamoto

“…Both patients harbored novel GATA2 frameshift variants predicted to eliminate the DNA-binding domain, suggesting a loss-of-function mechanism.ConclusionThese cases expand the phenotypic spectrum of GATA2 deficiency and highlight that atypical IBD-like symptoms, including Crohn’s disease-like presentations, may cause an initial manifestation. …”
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Genotype-Phenotype Correlations and Functional Outcomes in Pediatric Patients with KCNQ2-Related Epilepsy: A Multicenter Observational Study in Korea by Eon Ah Kim, Mi-Sun Yum, Seungbok Lee, Jae So Cho, Jeehun Lee, Byung Chan Lim

“…Although the mutational background of these disorders has been characterized, predicting outcomes based solely on genetic variants remains challenging. …”
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Association of dietary inflammatory index and vigorous physical activity on phenotypic age acceleration: a cross-sectional study with machine learning by Shuoqi Li, Jianming Zhou, Dandan Zhang, Qiuyu Du

“…Diet has also been hypothesized to influence aging by regulating inflammation. The phenotypic age acceleration (PhenoAgeAccel) reflects the difference between an individual’s phenotypic and chronological age; a positive value suggests accelerated aging, whereas a negative value indicates slower biological aging. …”
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Drone-Based Digital Phenotyping to Evaluating Relative Maturity, Stand Count, and Plant Height in Dry Beans (Phaseolus vulgaris L.) by Leonardo Volpato, Evan M. Wright, Francisco E. Gomez

“…The CNN-LSTM model showed high accuracy in RM prediction across various conditions, outperforming traditional image preprocessing approaches. …”
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Common Ɣ-chain cytokine receptors as functional phenotype markers of PD-1and TIM-3-positive T cells in multiple myeloma by E. V. Batorov, V. A. Aristova, G. Yu. Ushakova, S. A. Sizikova, V. V. Denisova, E. Ya. Shevela, A. A. Ostanin, E. R. Chernykh

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Digital Phenotyping for Detecting Depression Severity in a Large Payor-Provider System: Retrospective Study of Speech and Language Model Performance by Bradley Karlin, Doug Henry, Ryan Anderson, Salvatore Cieri, Michael Aratow, Elizabeth Shriberg, Michelle Hoy

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Safety Assessment of <i>Lactiplantibacillus plantarum</i> GUANKE Based on Whole-Genome Sequencing, Phenotypic, and Anti-Inflammatory Capacity Analysis by Simin Lu, Kun Yue, Siqin He, Yuanming Huang, Zhihong Ren, Jianguo Xu

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Application value of different frailty assessment tools in older patients undergoing major abdominal surgery by Junli You, Xuepiao Chen, Yu Rong, Sining Pan, Tianxiao Liu, Yubo Xie

“…This study aimed to compare the diagnostic performance of frail scale (FS), frailty phenotype (FP),11-item modified frailty index (mFI-11), Edmonton Frail Scale (EFS), and Tilburg Frailty Indicator (TFI) for frailty taking the comprehensive geriatric assessment (CGA) as the gold standard, and their ability to predict 30-day postoperative complications and prolonged length of stay (PLOS). …”
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Dynamic Lipid–Glycaemic Index and Inflammation—Endothelial Shifts and Fetal Aortic Wall Thickening: A Repeated-Measures Gestational Phenotyping Study by Maria Cezara Muresan, Biliana Belovan, Ioan Sîrbu, Zoran Laurentiu Popa, Cosmin Citu, Ioan Sas, Adrian Ratiu

“…Mothers were grouped by k-means clustering of the visit-to-visit change (Δ) in TG, TyG, hsCRP, IL-6 and FMD into three Metabolic-Inflammatory Response Phenotypes (MIRP-1/2/3). Linear mixed-effects models and extreme-gradient-boosting quantified associations and predictive performance. …”
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A Bayesian approach to incorporate structural data into the mapping of genotype to antigenic phenotype of influenza A(H3N2) viruses. by William T Harvey, Vinny Davies, Rodney S Daniels, Lynne Whittaker, Victoria Gregory, Alan J Hay, Dirk Husmeier, John W McCauley, Richard Reeve

“…Structure-guided variable selection thus improves confidence in the identification of genetic explanations of antigenic variation and we also show that prioritising the identification of causative mutations is not detrimental to the predictive capability of the analysis. Indeed, incorporating structural information into variable selection resulted in a model that could more accurately predict antigenic assay titres for phenotypically-uncharacterised virus from genetic sequence. …”
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Genomic prediction for tuberculosis resistance in dairy cattle. by Smaragda Tsairidou, John A Woolliams, Adrian R Allen, Robin A Skuce, Stewart H McBride, David M Wright, Mairead L Bermingham, Ricardo Pong-Wong, Oswald Matika, Stanley W J McDowell, Elizabeth J Glass, Stephen C Bishop

“…This paper focuses on prediction of resistance to bTB. We explore estimation of direct genomic estimated breeding values (DGVs) for bTB resistance in UK dairy cattle, using dense SNP chip data, and test these genomic predictions for situations when disease phenotypes are not available on selection candidates.…”
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Construction and Evaluation of a Cross-Regional and Cross-Year Monitoring Model for Millet Canopy Phenotype Based on UAV Multispectral Remote Sensing by Peng Zhao, Yuqiao Yan, Shujie Jia, Jie Zhao, Wuping Zhang

“…Incorporating multi-site and multi-year data further enhanced model robustness, increasing <i>R</i>² above 0.80 and markedly reducing average prediction errors. These findings demonstrate that rigorous radiometric calibration and appropriate vegetation index selection enable reliable UAV-based phenotyping for foxtail millet in diverse environments and time frames. …”
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P253 Next-generation phenotyping facilitates the identification of structural brain malformations in rare disorders through computational brain MRI analysis by Tzung-Chien Hsieh, Shriya Jaddu, Hannah Weiland, Merle ten Hagen, Jing-Mei Li, Chi-Chia Chang, Sun-Yuan Hsieh, Hsin-Hung Chou, Gholson Lyon, William Dobyns, Wei-Liang Chen

“…At the same time, patients with Ogden syndrome also demonstrated clear separation from controls, validating the potential of CBPS for phenotypic clustering and disease prediction. Conclusion: This study demonstrates the application of NGP to structural brain malformations in rare disorders. …”
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Integrated phenotypic and transcriptomic characterization of desmin-related cardiomyopathy in hiPSC-derived cardiomyocytes and machine learning-based classification of disease feat... by Vivien Batoumeni, Yeranuhi Hovhannisyan, Bénédicte Gobert, Keyhan Alvandipour, Jennifer Arthur Ataam, Ombline Conrad, David Hoffmann, Nicolas Wiest-Daesslé, Jochen Dobner, José Américo Nabuco Leva Ferreira Freitas, Hakim Hocini, Zhenlin Li, Laurent Brino, Andrea Rossi, Onnik Agbulut, Peter Sommer, Pierre Joanne, Konstantinos Gkatzis

“…Finaly, we developed a machine learning prediction model to classify cellular phenotypes, which can be used for translational research, including drug candidate screening. …”
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A novel missense variant of HS6ST2 gene in Paganini-Miozzo syndrome with a rare neurodevelopmental and Endocrine phenotypes by Meiling Zhang, Xiao Chang, Xiaoyun Du, Xiaoyun Du, Zhen Chen, Xinyue Zhang, Fucheng Cai

“…Comparison with previously reported cases revealed that different mutations might lead to different alternations in the function of HS6ST2 protein, resulting in distinct clinical phenotypes.…”
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