Search Results (115)

The formation of seedless traits is regulated by multiple factors. AGLs, which belong to the MADS-box family, were reported to be important regulators in this process; however, the underlying mechanism remains elusive. Here, we identified the VvAGL sub-family genes during the seed abortion process in seedless grapevine cv. ‘JingkeJing’ and found 40 differentially expressed VvAGL members and 1069 interacting proteins in this process. Interestingly, almost all members and their interacting proteins involved in the tryptophan metabolic pathway (K14486) and participated in the phytohormone signalling (KO04075) pathway, including the growth hormone (IAA), salicylic acid (SA), abscisic acid (ABA), cytokinin (CTK), and ethylene signalling pathways. The promoters of AGL sub-family genes contain cis-elements in response to hormones such as IAA, ABA, CTK, SA, and ETH, implying that they might respond to multi-hormone signals and involve in hormone signal transductions. Further expression analysis revealed VvAGL6-2, VvAGL11, VvAGL62-11, and VvAGL15 had the highest expression at the critical period of seed abortion, and there were positive correlations between ETH-VvAGL15-VvAGL6-2, ABA-VvAGL80, and SA-VvAGL62 in promoting seed abortion but negative feedback between IAA-VvAGL15-VvAGL6-2 and CTK-VvAGL11. Furthermore, many genes in the IAA, ABA, SA, CTK, and ETH pathways had a special expressional pattern in the seed, whereby we developed a regulatory network mediated by VvAGLs by responding to multihormonal crosstalk during grape seed abortion. Our findings provide new insights into the regulatory network of VvAGLs in multi-hormone signalling to regulate grape seed abortion, which could be helpful in the molecular breeding of high-quality seedless grapes. Full article

This study investigates a hybrid microelectromechanical system (MEMS) acoustic resonator through a hybrid approach to combine capacitive and piezoelectric transduction mechanisms, thus harnessing the advantages of both transducer technologies within a single device. By seamlessly integrating both piezoelectric and capacitive transducers, the newly designed hybrid resonators mitigate the limitations of capacitive and piezoelectric resonators. The unique hybrid configuration holds promise to significantly enhance overall device performance, particularly in terms of quality factor (Q-factor), insertion loss, and motional impedance. Moreover, the dual-transduction approach improves the signal-to-noise ratio and reduces feedthrough noise levels at higher frequencies. In this paper, the detailed design, complex fabrication processes, and thorough experimental validation are presented, demonstrating substantial performance enhancement potentials. A hybrid disk resonator with a single side-supporting anchor achieved an outstanding loaded Q-factor higher than 28,000 when operating under a capacitive drive and piezoelectric sense configuration. This is comparably higher than the measured Q-factor of 7600 for another disk resonator with two side-supporting anchors. The hybrid resonator exhibits a high Q-factor at its resonance frequency at 20 MHz, representing 2-fold improvement over the highest reported Q-factor for similar MEMS resonators in the literature. Also, the dual-transduction approach resulted in a more than 30 dB improvement in feedthrough suppression for devices with a 500 nm-thick ZnO layer, while hybrid resonators with a thicker piezoelectric layer of 1300 nm realized an even greater feedthrough suppression of more than 50 dB. The hybrid resonator integration strategy discussed offers an innovative solution for current and future advanced RF front-end applications, providing a versatile platform for future innovations in on-chip resonator technology. This work has the potential to lead to advancements in MEMS resonator technology, facilitating some significant improvements in multi-frequency and frequency agile RF applications through the original designs equipped with integrated capacitive and piezoelectric transduction mechanisms. The hybrid design also results in remarkable performance metrics, making it an ideal candidate for integrating next-generation wireless communication devices where size, cost, and energy efficiency are critical. Full article

Rice, a globally important food crop, faces significant challenges due to salt and drought stress. These abiotic stresses severely impact rice growth and yield, manifesting as reduced plant height, decreased tillering, reduced biomass, and poor leaf development. Recent advances in molecular biology and genomics have uncovered key physiological and molecular mechanisms that rice employs to cope with these stresses, including osmotic regulation, ion balance, antioxidant responses, signal transduction, and gene expression regulation. Transcription factors such as DREB, NAC, and bZIP, as well as plant hormones like ABA and GA, have been identified as crucial regulators. Utilizing CRISPR/Cas9 technology for gene editing holds promise for significantly enhancing rice stress tolerance. Future research should integrate multi-omics approaches and smart agriculture technologies to develop rice varieties with enhanced stress resistance, ensuring food security and sustainable agriculture in the face of global environmental changes. Full article

Recombinant adeno-associated viruses (rAAVs) play a pivotal role in the treatment of genetic diseases. However, current production and purification processes yield AAV-based preparations that often contain unwanted empty, partially filled or damaged viral particles and impurities, including residual host cell DNA and proteins, plasmid DNA, and viral aggregates. To precisely understand the composition of AAV preparations, we systematically compared four different single-stranded AAV (ssAAV) and self-complementary (scAAV) fractions extracted from the CsCl ultracentrifugation gradient using established methods (transduction efficiency, analytical ultracentrifugation (AUC), quantitative and digital droplet PCR (qPCR and ddPCR), transmission electron microscopy (TEM) and enzyme-linked immunosorbent assay (ELISA)) alongside newer techniques (multiplex ddPCR, multi-angle light-scattering coupled to size-exclusion chromatography (SEC-MALS), multi-angle dynamic light scattering (MADLS), and high-throughput sequencing (HTS)). Suboptimal particle separation within the fractions resulted in unexpectedly similar infectivity levels. No single technique could simultaneously provide comprehensive insights in the presence of both bioactive particles and contaminants. Notably, multiplex ddPCR revealed distinct vector genome fragmentation patterns, differing between ssAAV and scAAV. This highlights the urgent need for innovative analytical and production approaches to optimize AAV vector production and enhance therapeutic outcomes. Full article

This article develops a multi-stable hybrid energy harvester (MSHEH) which consists of a piezoelectric energy harvester (PEH) and an electromagnetic energy harvester (EMEH). By tuning two parameters, the MSHEH can achieve a mono-stable, bi-stable, and tri-stable state, respectively. A numerical procedure is developed to compute the EMEH’s transduction factor. The obtained result is validated experimentally. Using the equivalent magnetic 2-point dipole theory, the restoring force model of the magnetic spring is established. The obtained model is verified experimentally. The energy harvesting performances of the MSHEH under the four different configurations (linear, mono-stable, bi-stable and tri-stable) subjected to frequency sweep excitations are evaluated by simulation and validated by experiment. The comparative analysis focuses on power output, accumulated harvested energy, and effective energy-harvesting bandwidth. The optimum load resistances are investigated by Pareto front optimizations. The following key findings are obtained. When subjected to high-level frequency sweep excitation, the tri-stable configuration exhibits the widest frequency bandwidth and the highest total accumulated harvested energy. When subjected to low-level frequency sweep excitation, the bi-stable configuration is more efficient in energy harvesting. The best performance trade-off between the PEH and EMEH can be achieved by selecting the optimum load resistances properly. Full article

Cyber-physical systems (CPS) are vital in automating complex tasks across various sectors, yet they face significant vulnerabilities due to the rising threats of cybersecurity attacks. The recent surge in cyber-attacks on critical infrastructure (CI) and industrial control systems (ICSs), with a 150% increase in 2022 affecting over 150 industrial operations, underscores the urgent need for advanced cybersecurity strategies and education. To meet this requirement, we develop a specialised cyber-physical testbed (CPT) tailored for transportation CI, featuring a simplified yet effective automated level-crossing system. This hybrid CPT serves as a cost-effective, high-fidelity, and safe platform to facilitate cybersecurity education and research. High-fidelity networking and low-cost development are achieved by emulating the essential ICS components using single-board computers (SBC) and open-source solutions. The physical implementation of an automated level-crossing visualised the tangible consequences on real-world systems while emphasising their potential impact. The meticulous selection of sensors enhances the CPT, allowing for the demonstration of analogue transduction attacks on this physical implementation. Incorporating wireless access points into the CPT facilitates multi-user engagement and an infrared remote control streamlines the reinitialization effort and time after an attack. The SBCs overwhelm as traffic surges to 12 Mbps, demonstrating the consequences of denial-of-service attacks. Overall, the design offers a cost-effective, open-source, and modular solution that is simple to maintain, provides ample challenges for users, and supports future expansion. Full article

New process developments linked to Power to X (energy storage or energy conversion to another form of energy) require tools to perform process monitoring. The main gases involved in these types of processes are H₂, CO, CH₄, and CO₂. Because of the non-selectivity of the sensors, a multi-sensor matrix has been built in this work based on commercial sensors having very different transduction principles, and, therefore, providing richer information. To treat the data provided by the sensor array and extract gas mixture composition (nature and concentration), linear (Multi Linear Regression—Ordinary Least Square “MLR-OLS” and Multi Linear Regression—Partial Least Square “MLR-PLS”) and non-linear (Artificial Neural Network “ANN”) models have been built. The MLR-OLS model was disqualified during the training phase since it did not show good results even in the training phase, which could not lead to effective predictions during the validation phase. Then, the performances of MLR-PLS and ANN were evaluated with validation data. Good concentration predictions were obtained in both cases for all the involved analytes. However, in the case of methane, better prediction performances were obtained with ANN, which is consistent with the fact that the MOX sensor’s response to CH₄ is logarithmic, whereas only linear sensor responses were obtained for the other analytes. Finally, prediction tests performed on one-year aged sensor platforms revealed that PLS model predictions on aged platforms mainly suffered from concentration offsets and that ANN predictions mainly suffered from a drop of sensitivity. Full article

Biosensors play a crucial role in detecting cancer signals by orchestrating a series of intricate biological and physical transduction processes. Among various cancers, breast cancer stands out due to its genetic underpinnings, which trigger uncontrolled cell proliferation, predominantly impacting women, and resulting in significant mortality rates. The utilization of biosensors in predicting survival time becomes paramount in formulating an optimal treatment strategy. However, conventional biosensors employing traditional machine learning methods encounter challenges in preprocessing features for the learning task. Despite the potential of deep learning techniques to automatically extract useful features, they often struggle to effectively leverage the intricate relationships between features and instances. To address this challenge, our study proposes a novel smart biosensor architecture that integrates a multi-view multi-way graph learning (MVMWGL) approach for predicting breast cancer survival time. This innovative approach enables the assimilation of insights from gene interactions and biosensor similarities. By leveraging real-world data, we conducted comprehensive evaluations, and our experimental results unequivocally demonstrate the superiority of the MVMWGL approach over existing methods. Full article

Protein kinases (PKs) are involved in many intracellular signal transduction pathways through phosphorylation cascades and have become intensely investigated pharmaceutical targets over the past two decades. Inhibition of PKs using small-molecular inhibitors is a premier strategy for the treatment of diseases in different therapeutic areas that are caused by uncontrolled PK-mediated phosphorylation and aberrant signaling. Most PK inhibitors (PKIs) are directed against the ATP cofactor binding site that is largely conserved across the human kinome comprising 518 wild-type PKs (and many mutant forms). Hence, these PKIs often have varying degrees of multi-PK activity (promiscuity) that is also influenced by factors such as single-site mutations in the cofactor binding region, compound binding kinetics, and residence times. The promiscuity of PKIs is often—but not always—critically important for therapeutic efficacy through polypharmacology. Various in vitro and in vivo studies have also indicated that PKIs have the potential of interacting with additional targets other than PKs, and different secondary cellular targets of individual PKIs have been identified on a case-by-case basis. Given the strong interest in PKs as drug targets, a wealth of PKIs from medicinal chemistry and their activity data from many assays and biological screens have become publicly available over the years. On the basis of these data, for the first time, we conducted a systematic search for non-PK targets of PKIs across the human kinome. Starting from a pool of more than 155,000 curated human PKIs, our large-scale analysis confirmed secondary targets from diverse protein classes for 447 PKIs on the basis of high-confidence activity data. These PKIs were active against 390 human PKs, covering all kinase groups of the kinome and 210 non-PK targets, which included other popular pharmaceutical targets as well as currently unclassified proteins. The target distribution and promiscuity of the 447 PKIs were determined, and different interaction profiles with PK and non-PK targets were identified. As a part of our study, the collection of PKIs with activity against non-PK targets and the associated information are made freely available. Full article

Cadmium (Cd) pollution has become a global issue due to industrial and agricultural developments. However, the molecular mechanism of Cd-induced detrimental effects and relevant signal transduction/metabolic networks are largely unknown in marine fishes. Here, greenfin horse-faced filefish (Thamnaconus septentrionalis) were exposed to 5.0 mg/L Cd up to 7 days. We applied both biochemical methods and multi-omics techniques to investigate how the gills respond to Cd exposure. Our findings revealed that Cd exposure caused the formation of reactive oxygen species (ROS), which in turn activated the MAPK and apoptotic pathways to alleviate oxidative stress and cell damage. Glycolysis, protein degradation, as well as fatty acid metabolism might assist to meet the requirements of nutrition and energy under Cd stress. We also found that long-term (7 days, “long-term” means compared to 12 and 48 h) Cd exposure caused the accumulation of succinate, which would in turn trigger an inflammatory response and start an immunological process. Moreover, ferroptosis might induce inflammation. Overall, Cd exposure caused oxidative stress, energy metabolism disturbance, and immune response in greenfin horse-faced filefish. Our conclusions can be used as references for safety risk assessment of Cd to marine economic fishes. Full article

Tassel weight (TW) is a crucial agronomic trait that significantly affects pollen supply and grain yield development in maize breeding. To improve maize yield and develop new varieties, a comprehensive understanding of the genetic mechanisms underlying tassel weight is essential. In this study, tropical maize inbred lines, namely CML312, CML373, CML444, and YML46, were selected as female parents and crossed with the elite maize inbred line Ye107, which served as the common male parent, to develop a multi-parent population comprising four F8 recombinant inbred line (RIL) subpopulations. Using 6616 high-quality single nucleotide polymorphism (SNP) markers, we conducted genome-wide association analysis (GWAS) and genomic selection (GS) on 642 F8 RILs in four subpopulations across three different environments. Through GWAS, we identified 16 SNPs that were significantly associated with TW, encompassing two stable loci expressed across multiple environments. Furthermore, within the candidate regions of these SNPs, we discovered four novel candidate genes related to TW, namely Zm00001d044362, Zm00001d011048, Zm00001d011049, and Zm00001d031173 distributed on chromosomes 1, 3, and 8, which have not been previously reported. These genes are involved in processes such as signal transduction, growth and development, protein splicing, and pollen development, all of which play crucial roles in inflorescence meristem development, directly affecting TW. The co-localized SNP, S8_137379725, on chromosome 8 was situated within a 16.569 kb long terminal repeat retrotransposon (LTR-RT), located 22.819 kb upstream and 26.428 kb downstream of the candidate genes (Zm00001d011048 and Zm00001d011049). When comparing three distinct GS models, the BayesB model demonstrated the highest accuracy in predicting TW. This study establishes the theoretical foundation for future research into the genetic mechanisms underlying maize TW and the efficient breeding of high-yielding varieties with desired tassel weight through GS. Full article

Few-shot learning aims to solve the difficulty in obtaining training samples, leading to high variance, high bias, and over-fitting. Recently, graph-based transductive few-shot learning approaches supplement the deficiency of label information via unlabeled data to make a joint prediction, which has become a new research hotspot. Therefore, in this paper, we propose a novel ensemble semi-supervised few-shot learning strategy via transductive network and Dempster–Shafer (D-S) evidence fusion, named ensemble transductive propagation networks (ETPN). First, we present homogeneity and heterogeneity ensemble transductive propagation networks to better use the unlabeled data, which introduce a preset weight coefficient and provide the process of iterative inferences during transductive propagation learning. Then, we combine the information entropy to improve the D-S evidence fusion method, which improves the stability of multi-model results fusion from the pre-processing of the evidence source. Third, we combine the L2 norm to improve an ensemble pruning approach to select individual learners with higher accuracy to participate in the integration of the few-shot model results. Moreover, interference sets are introduced to semi-supervised training to improve the anti-disturbance ability of the mode. Eventually, experiments indicate that the proposed approaches outperform the state-of-the-art few-shot model. The best accuracy of ETPN increases by 0.3% and 0.28% in the 5-way 5-shot, and by 3.43% and 7.6% in the 5-way 1-shot on miniImagNet and tieredImageNet, respectively. Full article

The dissection of the genetic architecture and the detection of the loci for adaptive traits are important for marker-assisted selection (MAS) for breeding. A spring wheat diversity panel with 251 cultivars, mainly from China, was obtained to conduct a genome-wide association study (GWAS) to detect the new loci, including the heading date (HD), maturating date (MD), plant height (PH), and lodging resistance (LR). In total, 41 loci existing in all 21 chromosomes, except for 4A and 6B, were identified, and each explained 4.3–18.9% of the phenotypic variations existing in two or more environments. Of these, 13 loci are overlapped with the known genes or quantitative trait loci (QTLs), whereas the other 28 are likely to be novel. The 1A locus (296.9–297.7 Mb) is a multi-effect locus for LR and PH, whereas the locus on chromosome 6D (464.5–471.0 Mb) affects both the HD and MD. Furthermore, four candidate genes for adaptive traits were identified, involved in cell division, signal transduction, and plant development. Additionally, two competitive, allele-specific PCR (KASP) markers, Kasp_2D_PH for PH and Kasp_6D_HD for HD, were developed and validated in another 162 spring wheat accessions. Our study uncovered the genetic basis of adaptive traits and provided the associated SNPs and varieties with more favorable alleles for wheat MAS breeding. Full article

Diapause, an adaptative strategy for survival under harsh conditions, is a dynamic multi-stage process. Bombus terrestris, an important agricultural pollinator, is declining in the wild, but artificial breeding is possible by imitating natural conditions. Mated queen bees enter reproductive diapause in winter and recover in spring, but the regulatory mechanisms remain unclear. Herein, we conducted a comparative 4D label-free proteomic analysis of queen bees during artificial breeding at seven timepoints, including pre-diapause, diapause, and post-diapause stages. Through bioinformatics analysis of proteomic and detection of substance content changes, our results found that, during pre-diapause stages, queen bees had active mitochondria with high levels of oxidative phosphorylation, high body weight, and glycogen and TAG content, all of which support energy consumption during subsequent diapause. During diapause stages, body weight and water content were decreased but glycerol increased, contributing to cold resistance. Dopamine content, immune defense, and protein phosphorylation were elevated, while fat metabolism, protein export, cell communication, signal transduction, and hydrolase activity decreased. Following diapause termination, JH titer, water, fatty acid, and pyruvate levels increased, catabolism, synaptic transmission, and insulin signaling were stimulated, ribosome and cell cycle proteins were upregulated, and cell proliferation was accelerated. Meanwhile, TAG and glycogen content decreased, and ovaries gradually developed. These findings illuminate changes occurring in queen bees at different diapause stages during commercial production. Full article

Blueberry is a high-quality fruit tree with significant nutritional and economic value, but the intricate mechanism of sugar accumulation in its fruit remains unclear. In this study, the ripe fruits of blueberry cultivars ‘Anna’ and ‘Misty’ were utilized as experimental materials, and physiological and multi-omics methodologies were applied to analyze the regulatory mechanisms of the difference in sugar content between them. The results demonstrated that the ‘Anna’ fruit was smaller and had less hardness than the ‘Misty’ fruit, as well as higher sugar content, antioxidant capability, and lower active substance content. A total of 7067 differentially expressed genes (DEGs) (3674 up-regulated and 3393 down-regulated) and 140 differentially abundant metabolites (DAMs) (82 up-regulated and 58 down-regulated) were identified between the fruits of the two cultivars. According to KEGG analysis, DEGs were primarily abundant in phenylpropanoid synthesis and hormone signal transduction pathways, whereas DAMs were primarily enriched in ascorbate and aldarate metabolism, phenylpropanoid biosynthesis, and the pentose phosphate pathway. A combined multi-omics study showed that 116 DEGs and 3 DAMs in starch and sucrose metabolism (48 DEGs and 1 DAM), glycolysis and gluconeogenesis (54 DEGs and 1 DAM), and the pentose phosphate pathway (14 DEGs and 1 DAM) were significantly enriched. These findings suggest that blueberries predominantly increase sugar accumulation by activating carbon metabolism network pathways. Moreover, we identified critical transcription factors linked to the sugar response. This study presents new understandings regarding the molecular mechanisms underlying blueberry sugar accumulation and will be helpful in improving blueberry fruit quality through breeding. Full article