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Muscles are capable of modulating the body and adapting to environmental changes with a highly integrated sensing and actuation. Inspired by biological muscles, coiled/twisted fibers are adopted that can convert volume expansion into axial contraction and offer the advantages of flexibility and light weight. However, the sensing-actuation integrated fish line/yarn-based artificial muscles are still barely reported due to the poor actuation-sensing interface with off-the-shelf fibers. We report herein artificial coiled yarn muscles with self-sensing and actuation functions using the commercially available yarns. Via a two-step process, the artificial coiled yarn muscles are proved to obtain enhanced electrical conductivity and durability, which facilitates the long-term application in human-robot interfaces. The resistivity is successfully reduced from 172.39 Ω·cm (first step) to 1.27 Ω·cm (second step). The multimode sense of stretch strain, pressure, and actuation-sensing are analyzed and proved to have good linearity, stability and durability. The muscles could achieve a sensitivity (gauge factor, GF) of the contraction strain perception up to 1.5. We further demonstrate this self-aware artificial coiled yarn muscles could empower non-active objects with actuation and real-time monitoring capabilities without causing damage to the objects. Overall, this work provides a facile and versatile tool in improving the actuation-sensing performances of the artificial coiled yarn muscles and has the potential in building smart and interactive soft actuation systems.

Circular RNA (circRNA) vaccines have attracted increasing attention due to their stable closed-loop structures and persistent protein expression ability. During the synthesis process, nicked circRNAs with similar molecular weights to those of circRNAs are generated. Analytical techniques based on differences in molecular weight, such as capillary electrophoresis, struggle to distinguish between circRNAs and nicked circRNAs. The characteristic degradation products of circRNAs and their biological activities remain unclear. Therefore, developing methods to identify target circRNAs and non-target components and investigating degradation patterns will be beneficial to gaining an in-depth understanding of the properties and quality control of circRNAs vaccines. The reversed-phase HPLC (RP-HPLC) method was established for identification of target circRNAs, product-related substances, and impurities. Subsequently, we investigated the degradation patterns of circRNAs under thermal acceleration conditions and performed biological analysis of degradation products and linear precursors. Here, RP-HPLC method effectively identified circRNAs and nicked circRNAs. With thermal acceleration, circRNAs exhibited a “circular→nicked circRNAs→degradation products” degradation pattern. Biological analysis revealed that the immunogenicity of degradation products significantly decreased, whereas linear precursors did not possess immunogenicity. Thus, our established RP-HPLC method can be used for purity analysis of circRNA vaccines, which contributes to the quality control of circRNA vaccines and promoting the development of circRNA technology.

Huanglongbing (HLB) is a severe citrus disease worldwide. Wild Australian limes like Citrus australasica, C. inodora, and C. glauca possess beneficial HLB resistance traits. Individual trees of the three taxa were extensively used in a breeding program for over a decade to introgress resistance traits into commercial-quality citrus germplasm. We generated high-quality, phased, de novo genome assemblies of the three Australian limes using PacBio long-read sequencing. The genome assembly sizes of the primary and alternate haplotypes were determined for C. australasica (337 Mb/335 Mb), C. inodora (304 Mb/299 Mb), and C. glauca (376 Mb/379 Mb). The nine chromosome-scale scaffolds included 86–91% of the genome sequences generated. The integrity and completeness of the assembled genomes were estimated to be at 97.2–98.8%. Gene annotation studies identified 25,461 genes in C. australasica, 27,665 in C. inodora, and 30,067 in C. glauca. Genes belonging to 118 orthogroups were specific to Australian lime genomes compared to other citrus genomes analyzed. Significantly fewer canonical resistance (R) genes were found in C. inodora and C. glauca (319 and 449, respectively) compared to C. australasica (576), C. clementina (579), and C. sinensis (651). Similar patterns were observed for other gene families associated with potential HLB resistance, including Phloem protein 2 (PP2) and Callose synthase (CalS) genes predicted in the Australian lime genomes. The genomic information on Australian limes developed in the present study will help understand the genetic basis of HLB resistance.

Timely and reliable estimation of forest stock volume is essential for sustainable forest management and conservation. Light detection and ranging (LiDAR) data can provide an effective depiction of the three-dimensional structure information of forests, but its large-scale application is hampered by spatial continuity. This study aims to construct a LiDAR sampling framework, combined with multi-sensor imagery, to estimate the regional forest stock volume of natural secondary forests in Northeast China. Two sampling approaches were compared, including systematic sampling and classification-based sampling. First, the forest stock volume was mapped using a combination of field measurement data and full-coverage LiDAR data. Then, the forest stock volume obtained in the first step of estimation was used as a reference value, and optical images and topographic features were combined for secondary modeling to compare the effectiveness and accuracy of different sampling methods, including 12 systematic sampling and classification-based sampling methods. Our results show that the root mean square error (RMSE) of the 12 systematic sampling approaches ranged from 55.81 to 57.42 m3/ha, and the BIAS ranged from 21.55 to 24.89 m3/ha. The classification-based LiDAR sampling approach outperformed systematic sampling, with an RMSE of 55.56 (3/ha) and a BIAS of 20.68 (3/ha). This study compares different LiDAR sampling approaches and explores an effective LiDAR sample collection scheme for estimating forest stock, while balancing cost and accuracy. The classification-based LiDAR sampling approach described in this study is easy to apply and portable and can provide a reference for future LiDAR sample collection.

The rapid mutation and spread of SARS-CoV-2 variants recently, especially through the emerging variants Omicron BA5, BF7, XBB and BQ1, necessitate the development of universal vaccines to provide broad spectrum protection against variants. For the SARS-CoV-2 universal recombinant protein vaccines, an effective approach is necessary to design broad-spectrum antigens and combine them with novel adjuvants that can induce high immunogenicity. In this study, we designed a novel targeted retinoic acid-inducible gene-I (RIG-I) receptor 5′triphosphate double strain RNA (5′PPP dsRNA)-based vaccine adjuvant (named AT149) and combined it with the SARS-CoV-2 Delta and Omicron chimeric RBD-dimer recombinant protein (D-O RBD) to immunize mice. The results showed that AT149 activated the P65 NF-κB signaling pathway, which subsequently activated the interferon signal pathway by targeting the RIG-I receptor. The D-O RBD + AT149 and D-O RBD + aluminum hydroxide adjuvant (Al) + AT149 groups showed elevated levels of neutralizing antibodies against the authentic Delta variant, and Omicron subvariants, BA1, BA5, and BF7, pseudovirus BQ1.1, and XBB compared with D-O RBD + Al and D-O RBD + Al + CpG7909/Poly (I:C) groups at 14 d after the second immunization, respectively. In addition, D-O RBD + AT149 and D-O RBD + Al + AT149 groups presented higher levels of the T-cell-secreted IFN-γ immune response. Overall, we designed a novel targeted RIG-I receptor 5′PPP dsRNA-based vaccine adjuvant to significantly improve the immunogenicity and broad spectrum of the SARS-CoV-2 recombinant protein vaccine.

The accurate classification of single tree species in forests is important for assessing species diversity and estimating forest productivity. However, few studies have explored the influence of canopy morphological characteristics on the classification of tree species. Therefore, based on UAV LiDAR and hyperspectral data, in this study, we designed various classification schemes for the main tree species in the study area, i.e., birch, Manchurian ash, larch, Ulmus, and mongolica, in order to explore the effects of different data sources, classifiers, and canopy morphological features on the classification of a single tree species. The results showed that the classification accuracy of a single tree species using multisource remote sensing data was greater than that based on a single data source. The classification results of three different classifiers were compared, and the random forest and support vector machine classifiers exhibited similar classification accuracies, with overall accuracies above 78%. The BP neural network classifier had the lowest classification accuracy of 75.8%. The classification accuracy of all three classifiers for tree species was slightly improved when UAV LiDAR-extracted canopy morphological features were added to the classifier, indicating that the addition of canopy morphological features has a certain relevance for the classification of single tree species.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) protein subunit vaccine is one of the mainstream technology platforms for the development of COVID-19 vaccines, and most R&D units use the receptor-binding domain (RBD) or spike (S) protein as the main target antigen. The complexity of vaccine design, sequence, and expression systems makes it urgent to establish common antigen assays to facilitate vaccine development. In this study, we report the development of a double-antibody sandwich enzyme-linked immunosorbent assay (ELISA) to determine the antigen content of SARS-CoV-2 protein subunit vaccines based on the United States Pharmacopeia and ICH (international conference on harmonization) Q14 and Q2 (R2) requirements. A monoclonal antibody (mAb), 20D8, was identified as the detection antibody based on its high RBD binding activity (EC50 = 8.4 ng/mL), broad-spectrum anti-variant neutralizing activity (EC50: 2.7–9.8 ng/mL for pseudovirus and EC50: 9.6–127 ng/mL for authentic virus), good in vivo protection, and a recognized linear RBD epitope (369–379 aa). A porcine anti-RBD polyclonal antibody was selected as the coating antibody. Assay performance met the requirements of the analytical target profile with an accuracy and precision of ≥90% and adequate specificity. Within the specification range of 70–143%, the method capability index was >0.96; the misjudgment probability was

Preharvest fruit drop is a significant physiological problem that affects numerous commercially significant apple varieties, including ‘Gala.’ AVG and 1-MCP are two plant growth regulators commonly used to reduce fruit drop by reducing ethylene synthesis and perception, respectively. To optimize yield and market acceptance, a complete investigation of AVG and 1-MCP impacts on fruit drop and fruit quality of ‘Gala’ apples is required. In this study, four trials were conducted over the course of three years to determine the effects of AVG and 1-MCP on fruit drop and quality at harvest and after cold storage. Our results indicated that applications of AVG at the full-rate (130 mgL−1) three weeks before harvest (WBAH) were more effective at minimizing fruit drop than applications at the half-rate (65 mgL−1) and did not differ significantly from the double rate (260 mgL−1). Additionally, a single application of AVG was as effective in preventing fruit drop as two applications of 1-MCP. We also demonstrated that AVG decreased fruit skin pigmentation when used alone or in conjunction with GA4+7 or 1-MCP, while 1-MCP applications had no negative effect on fruit color. Finally, our data showed that when compared to 1-MCP and GA4+7, AVG alone was more effective in preventing stem-end splitting in Gala apples.

Small molecular nucleic acid drugs produce antiviral effects by activating pattern recognition receptors (PRRs). In this study, a small molecular nucleotide containing 5′triphosphoric acid (5′PPP) and possessing a double-stranded structure was designed and named nCoV-L. nCoV-L was found to specifically activate RIG-I, induce interferon responses, and inhibit duplication of four RNA viruses (Human enterovirus 71, Human poliovirus 1, Human coxsackievirus B5 and Influenza A virus) in cells. In vivo, nCoV-L quickly induced interferon responses and protected BALB/c suckling mice from a lethal dose of the enterovirus 71. Additionally, prophylactic administration of nCoV-L was found to reduce mouse death and relieve morbidity symptoms in a K18-hACE2 mouse lethal model of SARS-CoV-2. In summary, these findings indicate that nCoV-L activates RIG-I and quickly induces effective antiviral signals. Thus, it has potential as a broad-spectrum antiviral drug.

The research and development (R&D) of novel adjuvants is an effective measure for improving the immunogenicity of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) recombinant protein vaccine. Toward this end, we designed a novel single-stranded RNA-based adjuvant, L2, from the SARS-CoV-2 prototype genome. L2 could initiate retinoic acid-inducible gene-I signaling pathways to effectively activate the innate immunity. ZF2001, an aluminum hydroxide (Al) adjuvanted SARS-CoV-2 recombinant receptor binding domain (RBD) subunit vaccine with emergency use authorization in China, was used for comparison. L2, with adjuvant compatibility with RBD, elevated the antibody response to a level more than that achieved with Al, CpG 7909, or poly(I:C) as adjuvants in mice. L2 plus Al with composite adjuvant compatibility with RBD markedly improved the immunogenicity of ZF2001; in particular, neutralizing antibody titers increased by about 44-fold for Omicron, and the combination also induced higher levels of antibodies than CpG 7909/poly(I:C) plus Al in mice. Moreover, L2 and L2 plus Al effectively improved the Th1 immune response, rather than the Th2 immune response. Taken together, L2, used as an adjuvant, enhanced the immune response of the SARS-CoV-2 recombinant RBD protein vaccine in mice. These findings should provide a basis for the R&D of novel RNA-based adjuvants.

Forests are the largest terrestrial ecosystem with major benefits in three areas: economy, ecology, and society. However, the frequent occurrence of forest fires has seriously affected the structure and function of forests. To provide a strong scientific basis for forest fire prevention and control, Ripley’s K(d) function and the LightGBM algorithm were used to determine the spatial pattern of forest fires in four different provinces (Heilongjiang, Jilin, Liaoning, Hebei) in China from 2019 to 2021 and the impact of driving factors on different ecosystems. In addition, this study also identified fire hotspots in the four provinces based on kernel density estimation (KDE). An artificial neural network model (ANN) was created to predict the probability of occurrence of forest fires in the study area. The results showed that the forest fires were spatially clustered, but the variable importance of different factors varied widely among the different forest ecosystems. Forest fires in Heilongjiang and Liaoning Provinces were mainly caused by human-driven factors. For Jilin, meteorological factors were important in the occurrence of fires. Topographic and vegetation factors exhibited the greatest importance in Hebei Province. The selected driving factors were input to the ANN model to predict the probability of fire occurrence in the four provinces. The ANN model accurately captured 93.17%, 90.28%, 83.16%, and 89.18% of the historical forest fires in Heilongjiang, Jilin, Liaoning, and Hebei Provinces; Precision, Recall, and F-measure based on the full dataset are 0.87, 0.88, and 0.87, respectively. The results of this study indicated that there were differences in the driving factors of fire in different forest ecosystems. Different fire management policies must be formulated in response to this spatial heterogeneity.

Ethephon (ET) is an ethylene-releasing plant growth regulator (PGR) that can delay the bloom time in Prunus, thus reducing the risk of spring frost, which is exacerbated by global climate change. However, the adoption of ET is hindered by its detrimental effects on tree health. Little knowledge is available regarding the mechanism of how ET shifts dormancy and flowering phenology in peach. This study aimed to further characterize the dormancy regulation network at the transcriptional level by profiling the gene expression of dormant peach buds from ET-treated and untreated trees using RNA-Seq data. The results revealed that ET triggered stress responses during endodormancy, delaying biological processes related to cell division and intercellular transportation, which are essential for the floral organ development. During ecodormancy, ET mainly impeded pathways related to antioxidants and cell wall formation, both of which are closely associated with dormancy release and budburst. In contrast, the expression of dormancy-associated MADS (DAM) genes remained relatively unaffected by ET, suggesting their conserved nature. The findings of this study signify the importance of floral organogenesis during dormancy and shed light on several key processes that are subject to the influence of ET, therefore opening up new avenues for the development of effective strategies to mitigate frost risks.

Understanding the biochemical mechanisms underlying bud dormancy and bloom time regulation in deciduous woody perennials is critical for devising effective strategies to protect these species from spring frost damage. This study investigated the accumulation profiles of carbohydrates, ROS and antioxidants during dormancy in ‘Cripps Pink’ and ‘Honeycrisp’, two apple cultivars representing the early and late bloom cultivars, respectively. Our data showed that starch levels generally declined during dormancy, whereas soluble sugars increased. However, the present study did not record significant alternations in the carbohydrate accumulation profiles between the two cultivars that could account for the differences in their bloom dates. On the other hand, H2O2 accumulation patterns revealed an apparent correlation with the dormancy stage and bloom dates in both cultivars; peaking early in the early-blooming cultivar, sustaining high levels for a longer time in the late-blooming cultivars, and fading by the time of bud burst in both cultivars. Also, the redox balance during dormancy appeared to be maintained mainly by catalase and, to a lesser extent, by glutathione (GSH). Overall, the present study concludes that differences in ROS and the bud redox balance could, at least partially, explain the differences in dormancy duration and bloom date among apple cultivars.

Spring frosts exacerbated by global climate change have become a constant threat to temperate fruit production. Delaying the bloom date by plant growth regulators (PGRs) has been proposed as a practical frost avoidance strategy. Ethephon is an ethylene-releasing PGR found to delay bloom in several fruit species, yet its use is often coupled with harmful effects, limiting its applicability in commercial tree fruit production. Little information is available regarding the mechanisms by which ethephon influences blooming and bud dormancy. This study investigated the effects of fall-applied ethephon on bud phenology, cold hardiness, and hormonal balance throughout the bud dormancy cycle in peach. Our findings concluded that ethephon could alter several significant aspects of peach bud physiology, including accelerated leaf fall, extended chilling accumulation period, increased heat requirements, improved cold hardiness, and delayed bloom date. Ethephon effects on these traits were primarily dependent on its concentration and application timing, with a high concentration (500 ppm) and an early application timing (10% leaf fall) being the most effective. Endogenous ethylene levels were induced significantly in the buds when ethephon was applied at 10% versus 90% leaf fall, indicating that leaves are essential for ethephon uptake. The hormonal analysis of buds at regular intervals of chilling hours (CH) and growing degree hours (GDH) also indicated that ethephon might exert its effects through an abscisic acid (ABA)-independent way in dormant buds. Instead, our data signifies the role of jasmonic acid (JA) in mediating budburst and bloom in peach, which also appears to be influenced by ethephon treatment. Overall, this research presents a new perspective in interpreting horticultural traits in the light of biochemical and molecular data and sheds light on the potential role of JA in bud dormancy, which deserves further attention in future studies that aim at mitigating spring frosts.

Although low-intensity forest fires are a necessary part of healthy echo system, high-intensity forest fires continue to affect the diversity of forest ecosystems and species. Therefore, it is necessary to study the driving factors of forest fires and explore their possible locations and probabilities in complex forest terrain. In this article, we determined the relative influences of different types of factors on the occurrence of forest fires in Heilongjiang forest areas and compared the performance of artificial neural networks and logistic regression for wildfire prediction. By analyzing Heilongjiang forest fire data from 2002 to 2015 and constructing a model, we found that climate factors, topographical factors, and vegetation type factors play a crucial role in Heilongjiang’s wildfires. During the fire prevention period, temperature and wind speed have a more significant influence than other factors. According to the variable screening that we conducted, the model built by the variables that we used can predict 84% of forest fires in Heilongjiang Province. For recent wildfires (2019–2020) in most areas, we can use artificial neural networks for relatively accurate verification (85.2%). Therefore, artificial neural networks are very suitable for the prediction of forest fires in Heilongjiang Province. Through the prediction results, we also created a probability distribution map of fire occurrence in the study area. On this basis, we also analyzed the changes in the probability of natural fires under the weather changing trend, which can effectively aid in fire prevention and extinguishment.