Browse Articles
Discover research articles across all indexed journals
Development of a handheld GPU-assisted DSC-TransNet model for the real-time classification of plant leaf disease using deep learning approach
Design and Implementation of universal converter using ANN controller
In situ construction of MOF derived CoNC anchored on N-doped carbon xerogel sphere as efficient bifunctional ORR/OER electrocatalyst for Zn-air batteries
Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework
Influencing factors of hand, foot, and mouth disease based on structural equation modeling in Hubei, China
Publisher Correction: In silico comparative genomic analysis unravels a new candidate protein arsenal specifically associated with Fusarium oxysporum f. sp. albedinis pathogenesis
Direct collaboration between hospitals and NGOs, an essential tool to reinforce linkage to care in people living with HIV
Mendelian randomization analysis of plasma proteins reveals potential novel tumor markers for gastric cancer
Functional and genomic analyses of plant growth promoting traits in Priestia aryabhattai and Paenibacillus sp. isolates from tomato rhizosphere
Identifying factors that contribute to collision avoidance behaviours while walking in a natural environment
Author Correction: Rapid adsorption of selenium removal using iron manganese-based micro adsorbent
α-Copaene is a potent repellent against the Asian Citrus Psyllid Diaphorina citri
Abstract The Asian Citrus Psyllid (ACP), Diaphorina citri, severely threatens citrus production worldwide by transmitting the greening (= Huanglongbing)—causing bacterium Candidatus Liberibacter asiaticus. There is growing evidence that the push-pull strategy is suitable to partially mitigate HLB by repelling ACP with transgenic plants engineered to produce repellents and attracting the vector to plants with a minimal disease transmission rate. Species that pull ACP away from commercial citrus plants have been identified, and transgenic plants that repel ACP have been developed. The concept of a repellent-producing plant was first demonstrated with an Arabidopsis line engineered to overexpress a gene controlling the synthesis of β-caryophyllene and other sesquiterpenes. We have analyzed the volatile organic compounds released by this Arabidopsis line and identified α-humulene, α-copaene, and trace amounts of β-elemene, in addition to β-caryophyllene. Behavioral measurements demonstrated that α-copaene repels ACP at doses ca. 100× lower than those needed for β-caryophyllene repellence. In contrast, α-humulene is innocuous at the level emitted by the transgenic plant. We confirmed that a mixture of the three sesquiterpenes in the ratio 1:100:10 repels ACP. Likewise, a commercial sample of copaiba oil containing the three sesquiterpenes, in a proportion similar to that in the transgenic plant, repelled ACP.
Nanoscale nonlocal thermal transport and thermal field emission in high-current resonant tunnel structures
Impact of policy adjustments on low carbon transition strategies in construction using evolutionary game theory
Isolation of PCSK9-specific nanobodies from synthetic libraries using a combined protein selection strategy
Abstract Nanobodies (Nbs) hold great potential to replace conventional antibodies in various biomedical applications. However, conventional methods for their discovery can be time-consuming and expensive. We have developed a reliable protein selection strategy that combines magnetic activated cell sorting (MACS)-based screening of yeast surface display (YSD) libraries and functional ligand-binding identification by Tat-based recognition of associating proteins (FLI-TRAP) to isolate antigen-specific Nbs from synthetic libraries. This combined process enabled isolation of three unique Nb clones (NbT15, NbT21, and NbT22) that all bound specifically to a target antigen, namely proprotein convertase subtilisin/kexin type 9 (PCSK9) as well as a gain-of-function PCSK9 mutant (D374Y). All three clones bound to PCSK9 and blocked the interaction between the low-density lipoprotein receptor (LDLR) and either wild-type PCSK9 or the D374Y mutant. Overall, our combined protein selection method enables rapid and straightforward identification of potent antigen-specific Nbs in a manner that can be executed in a basic laboratory setting without the need for specialized equipment. We anticipate that our strategy will be a valuable addition to the protein engineering toolkit, allowing development of Nbs or virtually any other synthetic binding protein for a wide range of applications.
A structural health monitoring data reconstruction method based on VMD and SSA-optimized GRU model
Abstract In the field of Structural Health Monitoring (SHM), complete datasets are fundamental for modal identification analysis and risk prediction. However, data loss due to sensor failures, transmission interruptions, or hardware issues is a common problem. To address this challenge, this study develops a method combining Variational Mode Decomposition (VMD) and Sparrow Search Algorithm (SSA)-optimized Gate Recurrent Unit (GRU) for recovering structural response data. The methodology initially employs Variational Mode Decomposition (VMD) to preprocess and decompose the existing data from the target sensor into Intrinsic Mode Functions (IMFs) and residuals. Subsequently, the Gated Recurrent Unit (GRU) network utilizes data from other sensors to reconstruct the IMFs and residuals, ultimately producing the data reconstruction results. During this process, Singular Spectrum Analysis (SSA) is used to optimize the hyperparameters of the GRU network. To validate the effectiveness of this method, we utilized one month of monitoring data collected from a certain project and a publicly available dataset. On the public dataset, we tested performance at different data loss rates. Results show that, compared to a standalone GRU model and a VMD + GRU model, the VMD + SSA + GRU model’s reconstruction data root mean squared error is reduced by 46.61% and 32.57% on average, respectively, while the coefficient of determination increases by 38.74% and 18.50%. The data reconstruction method proposed in this study can accurately capture trends in missing data, without the need for manual hyperparameter tuning, and the reconstruction results are highly consistent with the real data.
Different keratoconus definitions can lead to substantial prevalence disparities in population-based studies
Abstract This report explores the prevalence of keratoconus in a population-based cohort of adults aged 40 or older according to ten different definitions. All Rotterdam Study participants with reliable Pentacam scans and no prior corneal refractive surgery were cross-sectionally analysed (n = 2660). First, we applied a novel evidence-based definition. Suspected keratoconus was defined as having at least one eye with a final D-index (BAD-D) ≥ 2.6. Manifest keratoconus was defined as having at least one eye with: (1) BAD-D ≥ 2.6; and (2) a score of at least 4/10 on the novel Rotterdam Keratoconus Scale (RKS); and (3) a confirming assessment of the relevant Pentacam maps; and (4) meeting Holladay’s criteria in case of recent contact lens usage. Using this proposed definition, 72 participants (2.71%, 95%CI: 2.16–3.40%) had suspected keratoconus, while 10 participants (0.38%, 95%CI: 0.20–0.69%) had manifest keratoconus. To assess reproducibility, two specialists independently applied the proposed definition, with a substantial inter-observer agreement (Kappa = 0.74). Interestingly, 6(60%) patients were unaware of having keratoconus. Applying nine alternative definitions from similar screening studies produced prevalence estimates ranging from 0.19 to 9.29% in the same cohort. Moreover, counting solely on a BAD-D cutoff of 2.6 to define keratoconus was unreliable, with a low positive predictive value of 14%. These findings explain partially the large heterogeneity in the reported keratoconus prevalences, underscoring the need for a standardized definition.