Full-Length Transcriptome Analysis Reveals Candidate Genes Involved in Terpenoid Biosynthesis in Artemisia argyi

Artemisia argyi is an important medicinal plant widely utilized for moxibustion heat therapy in China. The terpenoid biosynthesis process in A. argyi is speculated to play a key role in conferring its medicinal value. However, the molecular mechanism underlying terpenoid biosynthesis remains unclear, in part because the reference genome of A. argyi is unavailable. Moreover, the full-length transcriptome of A. argyi has not yet been sequenced. Therefore, in this study, de novo transcriptome sequencing of A. argyi's root, stem, and leaf tissues was performed to obtain those candidate genes related to terpenoid biosynthesis, by combining the PacBio single-molecule real-time (SMRT) and Illumina sequencing NGS platforms. And more than 55.4 Gb of sequencing data and 108,846 full-length reads (non-chimeric) were generated by the Illumina and PacBio platform, respectively. Then, 53,043 consensus isoforms were clustered and used to represent 36,820 non-redundant transcripts, of which 34,839 (94.62%) were annotated in public databases. In the comparison sets of leaves vs roots, and leaves vs stems, 13,850 (7,566 up-regulated, 6,284 down-regulated) and 9,502 (5,284 up-regulated, 4,218 down-regulated) differentially expressed transcripts (DETs) were obtained, respectively. Specifically, the expression profile and KEGG functional enrichment analysis of these DETs indicated that they were significantly enriched in the biosynthesis of amino acids, carotenoids, diterpenoids and flavonoids, as well as the metabolism processes of glycine, serine and threonine. Moreover, multiple genes encoding significant enzymes or transcription factors related to diterpenoid biosynthesis were highly expressed in the A. argyi leaves. Additionally, several transcription factor families, such as RLK-Pelle_LRR-L-1 and RLK-Pelle_DLSV, were also identified. In conclusion, this study offers a valuable resource for transcriptome information, and provides a functional genomic foundation for further research on molecular mechanisms underlying the medicinal use of A. argyi leaves

Tomato short internodes and pedicels encode an LRR receptor-like serine/threonine-protein kinase ERECTA regulating stem elongation through modulating gibberellin metabolism

Plant height is an important agronomic trait. Dwarf varieties present several advantages, such as lodging resistance, increased yield, and suitability for mechanized harvesting, which are crucial for crop improvement. However, limited research is available on dwarf tomato varieties suitable for production. In this study, we report a novel short internode mutant named “short internode and pedicel (sip)” in tomato, which exhibits marked internode and pedicel shortening due to suppressed cell elongation. This mutant plant has a compact plant structure and compact inflorescence, and has been demonstrated to produce more fruits, resulting in a higher harvest index. Genetic analysis revealed that this phenotype is controlled by a single recessive gene, SlSIP. BSA analysis and KASP genotyping indicated that ERECTA (ER) is the possible candidate gene for SlSIP, which encodes a leucine-rich receptor-like kinase. Additionally, we obtained an ER functional loss mutant using the CRISPR/Cas9 gene-editing technology. The 401st base A of ER is substituted with T in sip, resulting in a change in the 134th amino acid from asparagine (N) to isoleucine (I). Molecular dynamics(MD) simulations showed that this mutation site is located in the extracellular LRR domain and alters nearby ionic bonds, leading to a change in the spatial structure of this site. Transcriptome analysis indicated that the genes that were differentially expressed between sip and wild-type (WT) plants were enriched in the gibberellin metabolic pathway. We found that GA3 and GA4 decreased in the sip mutant, and exogenous GA3 restored the sip to the height of the WT plant. These findings reveal that SlSIP in tomatoes regulates stem elongation by regulating gibberellin metabolism. These results provide new insights into the mechanisms of tomato dwarfing and germplasm resources for breeding dwarfing tomatoes

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

How double dynamics affects the large deformation and fracture behaviors of soft materials

Numerous mechanically strong and tough soft materials comprising of polymer networks have been developed over the last two decades, motivated by new high-tech applications in engineering and bio-related fields. These materials are characterized by their dynamic complexities and large deformation behaviors. In this Review, we focus on how chain dynamics affects the large deformation and fracture behaviors of soft materials. To favor readers without a rheology background, first we review the linear rheology behaviors of several simple networks. We show that, by playing with the physical entanglement, chemical cross-linking, and physical association of the building polymers, a very rich panel of dynamic responses can be obtained. Then, we show examples of how chain dynamics affects the deformation and fracture behaviors of dually cross-linked hydrogels having chemical cross-linkers and physical bonds. We also provide examples on the unique deformation behavior of physical double-network gels made from triblock polymers. Thereafter, examples of the influence of chain dynamics on the crack initiation and growth behaviors are presented. We show that even for chemically cross-linked double-network hydrogels that exhibit elastic behaviors in a common deformation window, the chain dynamics influences the damage zone size at the crack tip. Finally, we conclude this Review by proposing several directions for future research

Mechanism of temperature-induced asymmetric swelling and shrinking kinetics in self-healing hydrogels

Understanding the physical principle that governs the stimuli-induced swelling and shrinking kinetics of hydrogels is indispensable for their applications. Here, we show that the shrinking and swelling kinetics of self-healing hydrogels could be intrinsically asymmetric. The structure frustration, formed by the large difference in the heat and solvent diffusions, remarkably slows down the shrinking kinetics. The plateau modulus of viscoelastic gels is found to be a key parameter governing the formation of structure frustration and, in turn, the asymmetric swelling and shrinking kinetics. This work provides fundamental understandings on the temperature-triggered transient structure formation in self-healing hydrogels. Our findings will find broad use in diverse applications of self-healing hydrogels, where cooperative diffusion of water and gel network is involved. Our findings should also give insight into the molecular diffusion in biological systems that possess macromolecular crowding environments similar to self-healing hydrogels

Structure Frustration Enables Thermal History-Dependent Responsive Behavior in Self-Healing Hydrogels

Biological soft tissues usually execute their functions via nonequilibrium and dynamic structural transformations. By contrast, functional hydrogels are mainly constructed by implementing static and equilibrium structures in the polymer network. Here, using polyampholyte hydrogel as a model system, we demonstrated that the nonequilibrium structure transformation in self-healing hydrogels enables the gels with many new features, including thermal history dependence, quick and asymmetric thermal response (instant transparent-to-turbid transition but slow turbid-to-transparent transition), tunable cloud point, tunable recovery time, and tiny changes in sample size and mechanical performance. These features make them distinct to conventional thermoresponsive hydrogels based on thermodynamic equilibrium and endow them with a new type of promising thermoresponsive materials. We revealed the structure change and studied the role of the thermal protocol on this thermoresponsive behavior by combining ultraviolet spectrum, small-angle X-ray scattering, theology, and mechanical measurements. We also presented two conceptual applications of this thermoresponsive hydrogel in thermal imaging and security paper. We believe that this work will inspire future research on creating functional hydrogels via nonequilibrium structure transformations

Analysis of a hazardous factory explosion accident

Factory explosion accidents not only cause damages of factory facilities and equipment, or casualties usually, but also cause fire disaster due to various reasons, resulting in larger losses. After the occurrence of accidents, timely, accurate, and efficient response is an urgent need to protect the safety of people's lives and property. A hazardous Factory Explosion accident in North China in 2019 was taken as an example by this paper. It conducts research from four aspects, including the basic situation of the accident, the handling of the explosion accident, and the analysis of the cause of explosion accident. The useful advice and efficient disposal methods of accidents, the enhancement of disaster prevention and disaster relief capabilities, and the formation of a long-term mechanism to ensure the safety of people’s lives and property are expounded in this paper, in order to provide a reference for similar accident management

High-Fidelity Hydrogel Thin Films Processed from Deep Eutectic Solvents

Polyampholyte (PA) hydrogels are a fascinating class of soft materials that can exhibit high toughness while retaining self-healing characteristics. This behavior results from the random distribution of oppositely charged monomers along the polymer chains that form transient bonds with a range of bond strengths. PAs can be dissolved in aqueous salt solutions and then recast via immersion precipitation, making them particularly useful as surface coatings in biomedical applications. Moreover, this immersion precipitation technique allows these PA hydrogels to be fabricated into films less than 100 nm. One critical challenge to this aqueous processing method is the recrystallization of the salt upon water evaporation. Such recrystallization can disrupt the hydrogel morphology especially in thin films. In this study, a deep eutectic solvent (DES) formed from urea and choline chloride was used to dissolve PAs made from p-styrenesulfonic acid sodium salt and 3-(methacryloylamino)propyl trimethylammonium chloride. This DES has a freezing point of 12 degrees C, allowing it to remain stable and liquid-like at room temperatures. Thus, these PAs can be processed in DES solutions, without this issue of recrystallization and with simple methods such as spin coating and dip coating. These methods allow these hydrogels to be used in thin (<100 nm)-film coating applications. Finally, the complete miscibility of DES in water allows a wider range of one-phase compositions and expands the processing window of these polyampholyte materials