Summary: Heat waves occurring during droughts can have a devastating impact on yield, especially if they happen during the flowering and seed set stages of the crop cycle. Global warming and climate change are driving an alarming increase in the frequency and intensity of combined drought and heat stress episodes, critically threatening global food security. Because high temperature is detrimental to reproductive processes, essential for plant yield, we measured the inner temperature, transpiration, sepal stomatal aperture, hormone concentrations and transcriptomic response of closed soybean flowers developing on plants subjected to a combination of drought and heat stress. Here, we report that, during a combination of drought and heat stress, soybean plants prioritize transpiration through flowers over transpiration through leaves by opening their flower stomata, while keeping their leaf stomata closed. This acclimation strategy, termed ‘differential transpiration’, lowers flower inner temperature by about 2–3°C, protecting reproductive processes at the expense of vegetative tissues. Manipulating stomatal regulation, stomatal size and/or stomatal density of flowers could serve as a viable strategy to enhance the yield of different crops and mitigate some of the current and future impacts of global warming and climate change on agriculture.

G2PDeep is an open-access web server, which provides a deep-learning framework for quantitative phenotype prediction and discovery of genomics markers. It uses zygosity or single nucleotide polymorphism (SNP) information from plants and animals as the input to predict quantitative phenotype of interest and genomic markers associated with phenotype. It provides a one-stop-shop platform for researchers to create deep-learning models through an interactive web interface and train these models with uploaded data, using high-performance computing resources plugged at the backend. G2PDeep also provides a series of informative interfaces to monitor the training process and compare the performance among the trained models. The trained models can then be deployed automatically. The quantitative phenotype and genomic markers are predicted using a user-selected trained model and the results are visualized. Our state-of-the-art model has been benchmarked and demonstrated competitive performance in quantitative phenotype predictions by other researchers. In addition, the server integrates the soybean nested association mapping (SoyNAM) dataset with five phenotypes, including grain yield, height, moisture, oil, and protein. A publicly available dataset for seed protein and oil content has also been integrated into the server. The G2PDeep server is publicly available at http://g2pdeep.org. The Python-based deep-learning model is available at https://github.com/shuaizengMU/G2PDeep_model.

BACKGROUND: Plasma concentration of PAI-1 (plasminogen activator inhibitor-1) correlates with arterial stiffness. Vascular smooth muscle cells (SMCs) express PAI-1, and the intrinsic stiffness of SMCs is a major determinant of total arterial stiffness. We hypothesized that PAI-1 promotes SMC stiffness by regulating the cytoskeleton and that pharmacological inhibition of PAI-1 decreases SMC and aortic stiffness. METHODS: PAI-039, a specific inhibitor of PAI-1, and small interfering RNA were used to inhibit PAI-1 expression in cultured human SMCs. Effects of PAI-1 inhibition on SMC stiffness, F-actin (filamentous actin) content, and cytoskeleton-modulating enzymes were assessed. WT (wild-type) and PAI-1–deficient murine SMCs were used to determine PAI-039 specificity. RNA sequencing was performed to determine the effects of PAI-039 on SMC gene expression. In vivo effects of PAI-039 were assessed by aortic pulse wave velocity. RESULTS: PAI-039 significantly reduced intrinsic stiffness of human SMCs, which was accompanied by a significant decrease in cytoplasmic F-actin content. PAI-1 gene knockdown also decreased cytoplasmic F-actin. PAI-1 inhibition significantly increased the activity of cofilin, an F-actin depolymerase, in WT murine SMCs, but not in PAI-1–deficient SMCs. RNA-sequencing analysis suggested that PAI-039 upregulates AMPK (AMP-activated protein kinase) signaling in SMCs, which was confirmed by Western blotting. Inhibition of AMPK prevented activation of cofilin by PAI-039. In mice, PAI-039 significantly decreased aortic stiffness and tunica media F-actin content without altering the elastin or collagen content. CONCLUSIONS: PAI-039 decreases intrinsic SMC stiffness and cytoplasmic stress fiber content. These effects are mediated by AMPK-dependent activation of cofilin. PAI-039 also decreases aortic stiffness in vivo. These findings suggest that PAI-1 is an important regulator of the SMC cytoskeleton and that pharmacological inhibition of PAI-1 has the potential to prevent and treat cardiovascular diseases involving arterial stiffening.

Background: Omicron (B.1.1.529), a variant of SARS-CoV-2, has emerged as a dominant strain in COVID-19 pandemic. This development has raised concerns about the effectiveness of vaccination to Omicron, particularly in the context of children and adolescents. Our study evaluated the efficacy of different COVID-19 vaccination regimens in children and adolescents during the Omicron epidemic phase.Methods: We searched PubMed, Cochrane, Web of Science, and Embase electronic databases for studies published through March 2023 on the association between COVID-19 vaccination and vaccine effectiveness (VE) against SARS-CoV-2 infection in children and adolescents at the Omicron variant period. The effectiveness outcomes included mild COVID-19 and severe COVID-19. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and was prospectively registered in PROSPERO (CRD42023390481).Results: A total of 33 studies involving 16,532,536 children were included in the analysis. First, in children and adolescents aged 0–19 years, the overall VE of the COVID-19 vaccine is 45% (95% confidence interval [CI]: 40 to 50%). Subgroup analysis of VE during Omicron epidemic phase for different dosage regimens demonstrated that the VE was 50% (95% CI: 44 to 55%) for the 2-dose vaccination and 61% (95% CI: 45 to 73%) for the booster vaccination. Upon further analysis of different effectiveness outcomes during the 2-dose vaccination showed that the VE was 41% (95% CI: 35 to 47%) against mild COVID-19 and 71% (95% CI: 60 to 79%) against severe COVID-19. In addition, VE exhibited a gradual decrease over time, with the significant decline in the efficacy of Omicron for infection before and after 90 days following the 2-dose vaccination, registering 54% (95% CI: 48 to 59%) and 34% (95% CI: 21 to 56%), respectively.Conclusion: During the Omicron variant epidemic, the vaccine provided protection against SARS-CoV-2 infection in children and adolescents aged 0–19 years. Two doses of vaccination can provide effective protection severe COVID-19, with booster vaccination additionally enhancing VE.

Signal peptides (SP) play a crucial role in protein translocation in cells. The development of large protein language models (PLMs) provides a new opportunity for SP prediction, especially for the categories with limited annotated data. We present a Parameter-Efficient Fine-Tuning (PEFT) framework for SP prediction, PEFT-SP, to effectively utilize pre-trained PLMs. We implanted low-rank adaptation (LoRA) into ESM-2 models to better leverage the protein sequence evolutionary knowledge of PLMs. Experiments show that PEFT-SP using LoRA enhances state-of-the-art results, leading to a maximum MCC2 gain of 0.372 for SPs with small training samples and an overall MCC2 gain of 0.048. Furthermore, we also employed two other PEFT methods, i.e., Prompt Tunning and Adapter Tuning, into ESM-2 for SP prediction. More elaborate experiments show that PEFT-SP using Adapter Tuning can also improve the state-of-the-art results with up to 0.202 MCC2 gain for SPs with small training samples and an overall MCC2 gain of 0.030. LoRA requires fewer computing resources and less memory compared to Adapter, making it possible to adapt larger and more powerful protein models for SP prediction.

Background: The emergence of new SARS-CoV-2 variants, the resulting reinfections, and post–COVID-19 condition continue to impact many people’s lives. Tracking websites like the one at Johns Hopkins University no longer report the daily confirmed cases, posing challenges to accurately determine the true extent of infections. Many COVID-19 cases with mild symptoms are self-assessed at home and reported on social media, which provides an opportunity to monitor and understand the progression and evolving trends of the disease. Objective: We aim to build a publicly available database of COVID-19–related tweets and extracted information about symptoms and recovery cycles from self-reported tweets. We have presented the results of our analysis of infection, reinfection, recovery, and long-term effects of COVID-19 on a visualization website that refreshes data on a weekly basis. Methods: We used Twitter (subsequently rebranded as X) to collect COVID-19–related data, from which 9 native English-speaking annotators annotated a training dataset of COVID-19–positive self-reporters. We then used large language models to identify positive self-reporters from other unannotated tweets. We used the Hibert transform to calculate the lead of the prediction curve ahead of the reported curve. Finally, we presented our findings on symptoms, recovery, reinfections, and long-term effects of COVID-19 on the Covlab website. Results: We collected 7.3 million tweets related to COVID-19 between January 1, 2020, and April 1, 2024, including 262,278 self-reported cases. The predicted number of infection cases by our model is 7.63 days ahead of the official report. In addition to common symptoms, we identified some symptoms that were not included in the list from the US Centers for Disease Control and Prevention, such as lethargy and hallucinations. Repeat infections were commonly occurring, with rates of second and third infections at 7.49% (19,644/262,278) and 1.37% (3593/262,278), respectively, whereas 0.45% (1180/262,278) also reported that they had been infected >5 times. We identified 723 individuals who shared detailed recovery experiences through tweets, indicating a substantially reduction in recovery time over the years. Specifically, the average recovery period decreased from around 30 days in 2020 to approximately 12 days in 2023. In addition, geographic information collected from confirmed individuals indicates that the temporal patterns of confirmed cases in states such as California and Texas closely mirror the overall trajectory observed across the United States. Conclusions: Although with some biases and limitations, self-reported tweet data serves as a valuable complement to clinical data, especially in the postpandemic era dominated by mild cases. Our web-based analytic platform can play a significant role in continuously tracking COVID-19, finding new uncommon symptoms, detecting and monitoring the manifestation of long-term effects, and providing necessary insights to the public and decision-makers.