Characterization of Small Interfering RNAs Derived from the Geminivirus/Betasatellite Complex Using Deep Sequencing

BACKGROUND: Small RNA (sRNA)-guided RNA silencing is a critical antiviral defense mechanism employed by a variety of eukaryotic organisms. Although the induction of RNA silencing by bipartite and monopartite begomoviruses has been described in plants, the nature of begomovirus/betasatellite complexes remains undefined. METHODOLOGY/PRINCIPAL FINDINGS: Solanum lycopersicum plant leaves systemically infected with Tomato yellow leaf curl China virus (TYLCCNV) alone or together with its associated betasatellite (TYLCCNB), and Nicotiana benthamiana plant leaves systemically infected with TYLCCNV alone, or together with TYLCCNB or with mutant TYLCCNB were harvested for RNA extraction; sRNA cDNA libraries were then constructed and submitted to Solexa-based deep sequencing. Both sense and anti-sense TYLCCNV and TYLCCNB-derived sRNAs (V-sRNAs and S-sRNAs) accumulated preferentially as 22 nucleotide species in infected S. lycopersicum and N. benthamiana plants. High resolution mapping of V-sRNAs and S-sRNAs revealed heterogeneous distribution of V-sRNA and S-sRNA sequences across the TYLCCNV and TYLCCNB genomes. In TYLCCNV-infected S. lycopersicum or N. benthamiana and TYLCCNV and βC1-mutant TYLCCNB co-infected N. benthamiana plants, the primary TYLCCNV targets were AV2 and the 5' terminus of AV1. In TYLCCNV and betasatellite-infected plants, the number of V-sRNAs targeting this region decreased and the production of V-sRNAs increased corresponding to the overlapping regions of AC2 and AC3, as well as the 3' terminal of AC1. βC1 is the primary determinant mediating symptom induction and also the primary silencing target of the TYLCCNB genome even in its mutated form. CONCLUSIONS/SIGNIFICANCE: We report the first high-resolution sRNA map for a monopartite begomovirus and its associated betasatellite using Solexa-based deep sequencing. Our results suggest that viral transcript might act as RDR substrates resulting in dsRNA and secondary siRNA production. In addition, the betasatellite affected the amount of V-sRNAs detected in S. lycopersicum and N. benthamiana plants

Euryale Ferox Seed-inspired Super-lubricated Nanoparticles for Treatment of Osteoarthritis

Osteoarthritis has been regarded as a typical lubrication deficiency related joint disease, which is characterized by the breakdown of articular cartilage at the joint surface and the inflammation of the joint capsule. Here, inspired by the structure of the fresh euryale ferox seed that possesses a slippery aril and a hard coat containing starchy kernel, a novel superlubricated nanoparticle, namely poly (3‐sulfopropyl methacrylate potassium salt)‐grafted mesoporous silica nanoparticles (MSNs‐NH2@PSPMK), is biomimicked and synthesized via a one‐step photopolymerization method. The nanoparticles are endowed with enhanced lubrication by the grafted PSPMK polyelectrolyte polymer due to the formation of tenacious hydration layers surrounding the negative charges, and simultaneously are featured with effective drug loading and release behavior as a result of the sufficient mesoporous channels in the MSNs. When encapsulated with an anti‐inflammatory drug diclofenac sodium (DS), the lubrication capability of the superlubricated nanoparticles is improved, while the drug release rate is sustained by increasing the thickness of PSPMK layer, which is simply achieved via adjustment of the precursor monomer concentration in the photopolymerization process. Additionally, the in vitro and in vivo experimental results show that the DS‐loaded MSNs‐NH2@PSPMK nanoparticles effectively protect the chondrocytes from degeneration, and thus, inhibit the development of osteoarthritis.Peer reviewe

Prediction of mixed oil concentration in product oil pipeline coupling oil mixing mechanism and data correction

Objective Batch pipelining of product oils inevitably leads to oil mixing at the junction of oils transmitted in sequential batches within the pipelines. This disruption affects the formulation of station dispatching plans and offtake schemes. Therefore, accurately predicting the concentration distribution of oil mixing sections is considered a crucial foundation for enhancing the control of oil mixing, reducing energy consumption for mixed oil treatment, and preventing oil quality incidents at stations. Despite this, multi-dimensional numerical models of oil mixing have exhibited several drawbacks. These include lengthy calculation processes, inefficiencies in long-distance pipeline applications, neglect of the oil mixing progression mechanism process due to traditional data-driven approaches, and violations of physical principles, low accuracy, and poor interpretability in their results. Methods Through analyzing the oil mixing progression mechanism, this study delved into the fundamental control equations and initial boundary conditions relevant to oil mixing progression. A physics-guided coupling loss function was constructed by coupling the automatic differential method with a deep learning model, enabling the confinement of model prediction results within the corresponding physical solution space of oil mixing progression. Following this, the initial numerical solutions for short pipeline sections were used to predict the mixed oil concentration distribution throughout long-distance transmission pipelines based on the recursive rolling prediction and data correction methods. Results The numerical examples demonstrated a higher accuracy of the established model than traditional data-driven methods, manifesting a notable 91% decrease in MAPE. This model also displayed reduced dependency on data and alleviated Root Mean Square Error (RMSE) fluctuations by 60% with changes in data size. Moreover, the computational expenses were minimized to a mere 12% of those incurred by the Fluent numerical simulation method. In practical engineering applications, MAE of the suggested framework substantially decreased by 71% and 58% respectively when compared to the Taylor model and the enhanced Taylor model. These findings underscored the effectiveness of the proposed prediction method in resolving the mixed oil concentration distribution in long-distance pipelines. Conclusion The proposed prediction method of mixed oil concentration in product oil pipelines couples oil mixing progression mechanism constraints with data correction. This method accurately and efficiently predicts mixed oil concentration distribution in long-distance pipelines, guiding the formulation of mixed oil receiving plans for stations and enhancing the intelligent control of oil mixing

Comparison of ziprasidone and olanzapine on cognitive function in patients with schizophrenia at different stages: a prospective study in Huai’an, China

ObjectiveTo compare the effects of ziprasidone and olanzapine on cognitive function in patients with first-episode schizophrenia and chronic schizophrenia at different stages.MethodsCognitive function tests were performed on chronic schizophrenic patients who took olanzapine for a long time, first-episode drug-free schizophrenic patients, and healthy controls.ResultsThere were significant differences in the digit span test, Stroop color and word test, auditory verbal learning test N2, N3, N4, trail-making test, verbal fluency test, and clock drawing test between first-episode drug-free schizophrenic patients and healthy controls (p < 0.05). Compared with patients with chronic schizophrenia, there were significant differences in the digit span test, Stroop color and word test B, auditory verbal learning test, trail making test B, and clock drawing test in patients with first-episode schizophrenia after 4 weeks of olanzapine treatment (p < 0.05). Compared with patients with chronic schizophrenia after 4 weeks of Ziprasidone treatment, patients with first-episode schizophrenia had significant differences in the digit span test, Stroop color, and word test, auditory verbal learning test N3, and clock drawing test after 4 weeks of olanzapine treatment (p < 0.05). Compared with patients with chronic schizophrenia who were treated with Ziprasidone for 12 weeks, there were significant differences in Stroop color and word test A, auditory verbal learning test N3, and clock drawing test in patients with first-episode schizophrenia after 4 weeks of olanzapine treatment (p < 0.05).ConclusionPatients with schizophrenia have cognitive dysfunction in the early stage of onset. The combination of ziprasidone and olanzapine can effectively improve cognitive dysfunction and promote the recovery of social functions of patients

The effects of vascular and endocardial endothelia on rat myocardial performance in physiological and pathological situations

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal

Facile synthesis, crystal structure and bioactivity evaluation of two novel barium complexes based on 2,4,6-trichlorophenoxyacetic acid and o-ferrocenylcarbonyl benzoic acid

With a microwave method, two novel Ba(ii) complexes were synthesized for the first time and their allelopathic and antifungal activity was evaluated.</p