Positive Darwinian selection is a driving force for the diversification of terpenoid biosynthesis in the genus Oryza

Background: Terpenoids constitute the largest class of secondary metabolites made by plants and display vast chemical diversity among and within species. Terpene synthases (TPSs) are the pivotal enzymes for terpenoid biosynthesis that create the basic carbon skeletons of this class. Functional divergence of paralogous and orthologous TPS genes is a major mechanism for the diversification of terpenoid biosynthesis. However, little is known about the evolutionary forces that have shaped the evolution of plant TPS genes leading to terpenoid diversity. Results: The orthologs of Oryza Terpene Synthase 1 (OryzaTPS1), a rice terpene synthase gene involved in indirect defense against insects in Oryza sativa, were cloned from six additional Oryza species. In vitro biochemical analysis showed that the enzymes encoded by these OryzaTPS1 genes functioned either as (E)-β-caryophyllene synthases (ECS), or (E)-β-caryophyllene & germacrene A synthases (EGS), or germacrene D & germacrene A synthases (DAS). Because the orthologs of OryzaTPS1 in maize and sorghum function as ECS, the ECS activity was inferred to be ancestral. Molecular evolutionary detected the signature of positive Darwinian selection in five codon substitutions in the evolution from ECS to DAS. Homology-based structure modeling and the biochemical analysis of laboratory-generated protein variants validated the contribution of the five positively selected sites to functional divergence of OryzaTPS1. The changes in the in vitro product spectra of OryzaTPS1 proteins also correlated closely to the changes in in vivoblends of volatile terpenes released from insect-damaged rice plants. Conclusions: In this study, we found that positive Darwinian selection is a driving force for the functional divergence of OryzaTPS1. This finding suggests that the diverged sesquiterpene blend produced by the Oryza species containing DASmay be adaptive, likely in the attraction of the natural enemies of insect herbivores

Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems

There has been a recent surge in the study of generating recommendations within the framework of causal inference, with the recommendation being treated as a treatment. This approach enhances our understanding of how recommendations influence user behaviour and allows for identification of the factors that contribute to this impact. Many researchers in the field of causal inference for recommender systems have focused on using propensity scores, which can reduce bias but may also introduce additional variance. Other studies have proposed the use of unbiased data from randomized controlled trials, though this approach requires certain assumptions that may be difficult to satisfy in practice. In this paper, we first explore the causality-aware interpretation of recommendations and show that the underlying exposure mechanism can bias the maximum likelihood estimation (MLE) of observational feedback. Given that confounders may be inaccessible for measurement, we propose using contrastive SSL to reduce exposure bias, specifically through the use of inverse propensity scores and the expansion of the positive sample set. Based on theoretical findings, we introduce a new contrastive counterfactual learning method (CCL) that integrates three novel positive sampling strategies based on estimated exposure probability or random counterfactual samples. Through extensive experiments on two real-world datasets, we demonstrate that our CCL outperforms the state-of-the-art methods.Comment: conferenc

Mortality and Pre-Hospitalization use of Renin-Angiotensin System Inhibitors in Hypertensive COVID-19 Patients.

Background There has been significant controversy regarding the effects of pre-hospitalization use of renin-angiotensin system (RAS) inhibitors on the prognosis of hypertensive COVID-19 patients. Methods and Results We retrospectively assessed 2,297 hospitalized COVID-19 patients at Tongji Hospital in Wuhan, China, from January 10th to March 30th, 2020; and identified 1,182 patients with known hypertension on pre-hospitalization therapy. We compared the baseline characteristics and in-hospital mortality between hypertensive patients taking RAS inhibitors (N=355) versus non-RAS inhibitors (N=827). Of the 1,182 hypertensive patients (median age 68 years, 49.1% male), 12/355 (3.4%) patients died in the RAS inhibitors group vs. 95/827 (11.5%) patients in the non-RAS inhibitors group (p<0.0001). Adjusted hazard ratio for mortality was 0.28 (95% CI 0.15-0.52, p<0.0001) at 45 days in the RAS inhibitors group compared with non-RAS inhibitors group. Similar findings were observed when patients taking angiotensin receptor blockers (N=289) or angiotensin converting enzyme inhibitors (N=66) were separately compared with non-RAS inhibitors group. The RAS inhibitors group compared with non-RAS inhibitors group had lower levels of C-reactive protein (median 13.5 vs. 24.4 pg/mL; p=0.007) and interleukin-6 (median 6.0 vs. 8.5 pg/mL; p=0.026) on admission. The protective effect of RAS inhibitors on mortality was confirmed in a meta-analysis of published data when our data were added to previous studies (odd ratio 0.44, 95% CI 0.29-0.65, p<0.0001). Conclusions In a large single center retrospective analysis we observed a protective effect of pre-hospitalization use of RAS inhibitors on mortality in hypertensive COVID-19 patients; which might be associated with reduced inflammatory response

HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization

Domain Generalization (DG) endeavors to create machine learning models that excel in unseen scenarios by learning invariant features. In DG, the prevalent practice of constraining models to a fixed structure or uniform parameterization to encapsulate invariant features can inadvertently blend specific aspects. Such an approach struggles with nuanced differentiation of inter-domain variations and may exhibit bias towards certain domains, hindering the precise learning of domain-invariant features. Recognizing this, we introduce a novel method designed to supplement the model with domain-level and task-specific characteristics. This approach aims to guide the model in more effectively separating invariant features from specific characteristics, thereby boosting the generalization. Building on the emerging trend of visual prompts in the DG paradigm, our work introduces the novel \textbf{H}ierarchical \textbf{C}ontrastive \textbf{V}isual \textbf{P}rompt (HCVP) methodology. This represents a significant advancement in the field, setting itself apart with a unique generative approach to prompts, alongside an explicit model structure and specialized loss functions. Differing from traditional visual prompts that are often shared across entire datasets, HCVP utilizes a hierarchical prompt generation network enhanced by prompt contrastive learning. These generative prompts are instance-dependent, catering to the unique characteristics inherent to different domains and tasks. Additionally, we devise a prompt modulation network that serves as a bridge, effectively incorporating the generated visual prompts into the vision transformer backbone. Experiments conducted on five DG datasets demonstrate the effectiveness of HCVP, outperforming both established DG algorithms and adaptation protocols

1-Chromonyl-5-Imidazolylpentadienone Demonstrates Anti-Cancer Action against TNBC and Exhibits Synergism with Paclitaxel

Curcumin has been well studied for its anti-oxidant, anti-inflammatory, and anti-cancer action. Its potential as a therapy is limited due to its low bioavailability and rapid metabolism. To overcome these challenges, investigators are developing curcumin analogs, nanoparticle formulations, and combining curcumin with other compounds or dietary components. In the present study, we used a 1-chromonyl-5-imidazolylpentadienone named KY-20-22 that contains both the pharmacophore of curcumin and 1,4 benzopyrone (chromone) moiety typical for flavonoids, and also included specific moieties to enhance the bioavailability. When we tested the in vitro effect of KY-20-22 in triple-negative breast cancer (TNBC) cell lines, we found that it decreased the cell survival and colony formation of MDA-MB-231 and MDA-MB-468 cells. An increase in mitochondrial reactive oxygen species was also observed in TNBC cells exposed to KY-20-22. Furthermore, KY-20-22 decreased epithelial-mesenchymal formation (EMT) as evidenced by the modulation of the EMT markers E-cadherin and N-cadherin. Based on the fact that KY-20-22 regulates interleukin-6, a cytokine involved in chemotherapy resistance, we combined it with paclitaxel and found that it synergistically induced anti-proliferative action in TNBC cells. The results from this study suggested that 1-chromonyl-5-imidazolylpentadienone KY-20-22 exhibited anti-cancer action in MDA-MB-231 and MDA-MB-468 cells. Future studies are required to evaluate the anti-cancer ability and bioavailability of KY-20-22 in the TNBC animal model