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2022 ◽  
Vol 218 ◽  
pp. 106035
Author(s):  
Xiao Zhou ◽  
Zhou Huang ◽  
Han Wang ◽  
Ganmin Yin ◽  
Yi Bao ◽  
...  

2022 ◽  
Vol 191 ◽  
pp. 116302
Author(s):  
Akshata K. Naik ◽  
Venkatanareshbabu Kuppili

HortScience ◽  
2022 ◽  
Vol 57 (2) ◽  
pp. 221-230
Author(s):  
David G. Tork ◽  
Neil O. Anderson ◽  
Donald L. Wyse ◽  
Kevin J. Betts

The genus Linum L. contains ≈200 primarily blue-flowered species, including several ornamentals, yet no reports exist regarding the cut flower potential of this genus. The objective of this study was to evaluate the cut flower potential of perennial flax cultivars (L. perenne L. ‘Blue Flax’ and ‘Sapphire’; Expt. 1, 2018) and accessions (L. austriacum L., L. lewisii Pursh., and L. perenne; Expt. 2, 2019), and record traits that will enable breeding and selection for improved cut flower performance. The mean vase life across both cultivars in Expt. 1 was 9.2 days. In Expt. 2, L. perenne had the longest average vase life (9.3 days), followed by L. austriacum (9.1 days) and L. lewisii (8.3 days). The floral preservative (Floralife 300) significantly increased vase life by an average of 1.7 days in Expt. 1, and 1.6 days in Expt. 2, and resulted in a significantly greater number of flowers (≈2x) in both experiments. Significant variation was observed among genotypes for most traits, including vase life (6.2 to 11.3 days) and number of flowers (1.3 to 10.5), highlighting the opportunities for improving the potential of cut flower perennial flax through breeding.


2022 ◽  
Vol 13 ◽  
pp. 13-23
Author(s):  
Cesar D. Lopez ◽  
Jessica Ding ◽  
David P. Trofa ◽  
H. John Cooper ◽  
Jeffrey A. Geller ◽  
...  

Cancers ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 435
Author(s):  
Arsela Prelaj ◽  
Mattia Boeri ◽  
Alessandro Robuschi ◽  
Roberto Ferrara ◽  
Claudia Proto ◽  
...  

(1) Background: In advanced non-small cell lung cancer (aNSCLC), programmed death ligand 1 (PD-L1) remains the only biomarker for candidate patients to immunotherapy (IO). This study aimed at using artificial intelligence (AI) and machine learning (ML) tools to improve response and efficacy predictions in aNSCLC patients treated with IO. (2) Methods: Real world data and the blood microRNA signature classifier (MSC) were used. Patients were divided into responders (R) and non-responders (NR) to determine if the overall survival of the patients was likely to be shorter or longer than 24 months from baseline IO. (3) Results: One-hundred sixty-four out of 200 patients (i.e., only those ones with PD-L1 data available) were considered in the model, 73 (44.5%) were R and 91 (55.5%) NR. Overall, the best model was the linear regression (RL) and included 5 features. The model predicting R/NR of patients achieved accuracy ACC = 0.756, F1 score F1 = 0.722, and area under the ROC curve AUC = 0.82. LR was also the best-performing model in predicting patients with long survival (24 months OS), achieving ACC = 0.839, F1 = 0.908, and AUC = 0.87. (4) Conclusions: The results suggest that the integration of multifactorial data provided by ML techniques is a useful tool to select NSCLC patients as candidates for IO.


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