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Using degenerate primers designed based on the conserved regions of the reported SLF/SFB genes, more than a dozen of different cDNA clones were isolated from pollen of three different apple S-genotypes by reverse transcription (RT)-PCR. The 5¢-and 3¢-rapid amplification of cDNA end in combination with RT-PCR allowed isolation of two fulllength cDNAs, whose corresponding genes (RJ28-2 and Mg15, later renamed SLF 1 and SLF 2 ) were specifically expressed in pollen in an S-haplotype-specific manner. PCR analysis of seven different cultivars further showed the linkage between SLF 1 and S 1 -RNase and between SLF 2 and S 2 -RNase. The predicted ORFs of the two genes encode two F-box proteins of 393 amino acids in length with 70% amino acid identity. These features are consistent with the supposition that SLF 1 and SLF 2 are good candidates for pollen S-genes. Phylogenetic trees for the SLF/SFB and S-RNase proteins reported in the Solanaceae, Scrophulariaceae and Rosaceae (including apple) were constructed. It was found that the SLFs and S-RNases from apple were more closely related to those from Petunia (a genus of the Solanaceae) and Antirrhinum (a genus of the Scrophulariaceae) than to those from Prunus (a genus of the Rosaceae), implying a potential co-evolution between SLF/SFB and S-RNase. Furthermore, six SLF-like genes that shared a high level of sequence similarity (amino acid identity from 68 to 72%) to SLFs were also isolated. Among them, RJ28, Jt24 and Jt24-4 were confirmed to be expressed specifically in pollen in an S-haplotype-unspecific manner. These results are discussed in relation to the possible evolution of the SLF/SFB in S-RNase-based self-incompatible species.

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Objectives We aimed to develop deep learning models using longitudinal chest X-rays (CXRs) and clinical data to predict in-hospital mortality of COVID-19 patients in the intensive care unit (ICU). Methods Six hundred fifty-four patients (212 deceased, 442 alive, 5645 total CXRs) were identified across two institutions. Imaging and clinical data from one institution were used to train five longitudinal transformer-based networks applying five-fold cross-validation. The models were tested on data from the other institution, and pairwise comparisons were used to determine the best-performing models. Results A higher proportion of deceased patients had elevated white blood cell count, decreased absolute lymphocyte count, elevated creatine concentration, and incidence of cardiovascular and chronic kidney disease. A model based on pre-ICU CXRs achieved an AUC of 0.632 and an accuracy of 0.593, and a model based on ICU CXRs achieved an AUC of 0.697 and an accuracy of 0.657. A model based on all longitudinal CXRs (both pre-ICU and ICU) achieved an AUC of 0.702 and an accuracy of 0.694. A model based on clinical data alone achieved an AUC of 0.653 and an accuracy of 0.657. The addition of longitudinal imaging to clinical data in a combined model significantly improved performance, reaching an AUC of 0.727 ( p = 0.039) and an accuracy of 0.732. Conclusions The addition of longitudinal CXRs to clinical data significantly improves mortality prediction with deep learning for COVID-19 patients in the ICU. Key Points • Deep learning was used to predict mortality in COVID-19 ICU patients. • Serial radiographs and clinical data were used. • The models could inform clinical decision-making and resource allocation. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-08588-8.

Lectins play diverse roles in physiological processes as biological recognition molecules. In this report, a gene encoding Tachypleus tridentatus Lectin (TTL) was inserted into an oncolytic vaccinia virus (oncoVV) vector to form oncoVV-TTL, which showed significant antitumor activity in a hepatocellular carcinoma mouse model. Furthermore, TTL enhanced oncoVV replication through suppressing antiviral factors expression such as interferon-inducible protein 16 (IFI16), mitochondrial antiviral signaling protein (MAVS) and interferon-beta (IFN-β). Further investigations revealed that oncoVV-TTL replication was highly dependent on ERK activity. This study might provide insights into a novel way of the utilization of TTL in oncolytic viral therapies.