Browse Articles

Retraction: A Bmi1-miRNAs cross-talk modulates chemotherapy response to 5-fluorouracil in breast cancer cells

Fumigant dazomet induces tobacco plant growth via changing the rhizosphere microbial community

Retraction: Synergistic Apoptosis of CML cells by buthionine sulfoximine and hydroxychavicol correlates with activation of AIF and GSH-ROS-JNK-ERK-iNOS pathway

Mechanistic studies of Ca2+-induced classical pyroptosis pathway promoting renal adhesion on calcium oxalate kidney stone formation

Is MOOC really effective? Exploring the outcomes of MOOC adoption and its influencing factors in a higher educational institution in China

Massive Open Online Course (MOOC) has gained widespread adoption across diverse educational domains and plays a crucial role in advancing educational equality. Nevertheless, skepticism surrounds the effectiveness of MOOC due to their notably low completion rates. To explore the outcomes of MOOC adoption in higher education and improve its application efficiency, this study compares MOOC with traditional course in terms of mean score and pass rate. The study examines the factors influencing MOOC performance within the context of higher education, utilizing the method of Partial Least Squares-Structural Equation Modeling (PLS-SEM). This study analyzed MOOC learning data from a college over a period of six years and a total of 4,282 Chinese college students participated in this study. The factor of learning environment was proposed for the first time, and it was proved to have a significant impact on learning behavior and MOOC performance in higher education. The results reveal that 1) MOOC has a lower pass rate than traditional course (including both compulsory and selective course); 2) MOOC has a lower mean score than selective course only; 3) we did not find a significant difference between MOOC and compulsory course in terms of the mean score; 4) Learning behavior, learning motivation, perceived value, learning environment, previous experience and self-regulation have significant and positive influences on MOOC performance in higher education. The study provides valuable insights that college administrators should pay attention to students’ learning environment, learning motivation and other factors while actively introducing MOOC.

Relationship of social capital, community identity, and perceived safety resilience during public emergencies in Shanghai

Purity control of simulated moving bed based on advanced fuzzy II controller

Simulated Moving Bed (SMB) is the optimal technology for chromatographic separation, but its process is complex and sensitive to numerous parameters that affect separation performance, making it difficult to control. In recent years, fuzzy controllers have been widely applied in industry due to their simplicity, robustness, and ease of implementation. However, traditional fuzzy controllers used in industry do not consider the error acceleration term. In steady-state conditions, error acceleration is typically slightly less than the target value. Introducing the acceleration term, albeit non-fuzzy, in a proactive fuzzy I-type controller often leads to an increase in steady-state values. The study shows that, compared to the advanced fuzzy I-type controller, the extraction accuracy for material B improved by an average of 0.7%, while the accuracy for material A increased by 0.1%. Compared to traditional fuzzy controllers, the extraction accuracy for material B improved by an average of 0.35%, while the accuracy for material A remained relatively stable. In terms of stability analysis concerning variations in moving bed parameters, the advanced fuzzy II-type controller exhibited greater stability than the I-type, with an average precision stability improvement of 0.6%. Traditional fuzzy controllers demonstrated pathological characteristics during fluctuations in the switching time parameter, whereas the advanced fuzzy II type controller-maintained stability.

Men’s knowledge of obstetrics danger sign and associated factors in low-income countries: a systematic review and meta-analysis

Exploratory study of serum protein biomarkers for sudden cardiac arrest using protein extension assay: A case-control study

Background Biomarkers associated with the occurrence of sudden cardiac arrest (SCA) are not currently utilized in clinical practice. We aimed to identify novel protein biomarkers associated with sudden cardiac arrest (SCA) using proteomic profiling and evaluate their predictive power alongside traditional cardiovascular risk factors. Methods A total of 42 SCA patients with medical causes, aged ≤ 65 years and whose initial rhythm was shockable, and 42 age- and sex-matched controls were analyzed. The initial serum samples obtained after emergency department visits were used for SCA cases. Using a protein extension assay, we identified significant biomarkers through correlation analysis with SCA and extracted proteins with no or weak correlation with the initial lactate level and arrest-to-sampling time to account for post-cardiac arrest changes. The area under the receiver operating characteristic curve (AUROC) was calculated to assess the predictive performance of the extracted proteins. Results Among the 246 distinct proteins that met quality criteria, 97 showed a strong correlation with SCA. Among these 97 proteins, 44 showed weak or no correlation with lactate levels, and 12 showed weak or no correlation with onset-to-sampling time. Two proteins (AXL receptor tyrosine kinase [AXL] and TIMP Metallopeptidase inhibitor 4 [TIMP-4]) met all the criteria for biomarker extraction. Both showed significant associations with SCA and enhanced predictive power when combined with traditional risk factors in multivariable analysis. The AUROC for the baseline model using traditional risk factors was 0.692 (95% confidence interval [CI] 0.578–0.806), which improved significantly with the addition of AXL and TIMP-4 (AUROC [95% CI] 0.891 [0.817–0.964] and 0.910 [0.910–0.997], respectively). Conclusion AXL and TIMP-4 may be crucial role in the early detection and risk assessment of SCA. Future research to verify the utility of AXL and TIMP-4 in large cohorts is warranted.

Explainable hybrid transformer for multi-classification of lung disease using chest X-rays

Protective effect of Pinacidil on hypoxic-reoxygenated cardiomyocytes in vitro and in vivo via HIF-1α/HRE pathway

Cardiomyocyte hypoxia-reoxygenation (HR) is considered as a major cause of heart failure. Pinacidil is a classic ATP sensitive potassium channel opener and plays a crucial role in cardiomyocyte HR injuries. However, the specific mechanism is poorly understood. We established HR rat model and introduced 5-Hydroxydecanoate (5-HD), N-(2-Mercaptopropionyl)-glycine (MPG), and Dimethylethylenediylglycine (DMOG) to investigate the protection of Pinacidil (P) on cardiomyocyte. HE staining, electron microscopy and JC-1 staining were used to observe mitochondrial structure and mitochondrial membrane potential (MMP). Reactive oxygen species (ROS), hypoxia-inducible factor-1α (HIF-1α), vascular endothelial growth factor A (VEGF-A), heme oxygenase-1 (HO-1), and induced nitric oxide synthase (iNOS) were analyzed in this study. Network pharmacology analysis and auto-docking were used to predict the possible target of Pinadicil under cardiomyocyte HR condition. The integrity of mitochondrial structure and MMP were effectively promoted in P and MPG+DMOG + P groups. ROS was significantly increased after HR, treatment with P or MPG+DMOG + P, the content of ROS was increased. The expressions of HIF-1α, VEGF-A, HO-1 and iNOS were significantly increased in P and MPG+DMOG + P groups compared with HR group. Docking results confirmed that prolyl hydroxylase (PHD) was the most possible target for unsaturated binding with Pinacidil guanidine. Altogether, these data indicate that Pinacidil up-regulated and activated HIF-1α protein to protect caridomyocytes against HR injuries and the mechanism may be related to Pinacidil guanidine binding to PHD.

Development and validation of an Immune-related Gene-based model for predicting prognosis and immunotherapy outcomes in hepatocellular carcinoma patients

Intelligent tuning method for service scheduling in electric power communication networks based on operational risk and QoS guarantee

In the operational planning of electric power communication networks, a well-structured service scheduling scheme based on the established network topology can significantly enhance the risk prevention capabilities of these networks. Since routing policies directly influence data transmission paths, routing optimization serves as an effective strategy for improving network performance by mitigating transmission risks and threats. This paper introduces an Intelligent Tuning Method for Service Scheduling in Electric Power Communication Networks Based on Operational Risk and Quality of Service (QoS) Guarantee. Based on a comprehensive assessment of service transmission reliability and time costs, a route satisfaction evaluation function model has been developed. Utilizing this model, an enhanced Risk-Time Ant Colony Optimization (RT-ACO) routing algorithm is proposed, which builds upon the traditional ant colony algorithm. The improvements to the ant colony algorithm are made in four key areas: the definition of heuristic information, the weighting of parameters, the state selection strategy, and the pheromone update strategy. These enhancements aim to achieve optimal routing scheduling based on risk information. At the same time, a reconfiguration algorithm for power optical communication networks, based on service priority, is proposed for specific service requests. This algorithm provides both a primary routing path and an alternate routing path for service transmission, ensuring the delivery of high-priority services even when both the primary and standby paths are unavailable. Simulation results from an actual power business communication network demonstrate that the algorithm outputs the main and alternate paths with the lowest risk costs. Additionally, the path satisfaction of the proposed algorithm is improved by 7.4% compared to the traditional ant colony algorithm. This improvement validates the accuracy and superiority of the proposed algorithm and offers a valuable reference for ensuring the reliable operation of power optical fiber communication network systems.

Ethoxychelerythrine as a potential therapeutic strategy targets PI3K/AKT/mTOR induced mitochondrial apoptosis in the treatment of colorectal cancer

Optimizing breeding strategies for early-maturing white maize through genetic diversity and population structure

Maize production and productivity in sub-Saharan Africa are constrained by various factors. Assessing the genetic diversity of newly developed elite inbred lines can help identify lines with desirable genes and explore genetic relatedness for heterotic breeding. The objectives of this study were to assess the level of genetic diversity, and population structure, and identify appropriate clustering methods for assigning maize inbreds into heterotic groups. Three hundred and seventy-six elite inbreds extracted from three source populations were genotyped using Diversity Array Technology (DArTtag) mid-density platform. Results from 1904 of 3,305 SNP marker obtained revealed average marker polymorphism information content (PIC) of 0.39, observed heterozygosity of 0.02, gene diversity of 0.37, minor allele frequency of 0.29, Shannon and Simpson indices of 6.86 and 949.09, respectively, and allele richness of 787.70. The optimum sub-population was three defined by an admixture-based model and principal component analysis. The average genetic distance was 0.303 varying from 0.03 (TZEI 2772 × TZEI 2761) to 0.372 (TZEI 2273 × TZEI 2832). For appropriate heterotic classification of the 376 elite inbreds, the use of IBS distance matrix and average linkage clustering method provided the highest cophenetic correlation coefficient (0.97). Three heterotic group (HG) were identified using IBS distance and average linkage clustering method with HG 1 have 188 inbreds, HG 2 having 137, and HG 3 having 59 inbreds. The pedigree-based phylogenetic tree showed substantial consistency with the heterotic groups identified. The F-statistics based on the underlying population structure revealed 10% variation among sub-populations and 90% variation within sub-populations with a moderate level of genetic differentiation (0.10). The elite inbred lines showed a high degree of genetic diversity, which could be beneficial for developing new, early-maturing white hybrids to mitigate production constraints in sub-Saharan Africa.

Frequency reconfigurable PIN diode-based Reuleaux-triangle-shaped monopole antenna for UWB/Ku band applications

Abstract This paper presents a frequency reconfigurable monopole antenna developed for UWB/Ku band applications. The design employs a microstrip-fed Reuleaux-triangle-shaped patch with a defected ground structure. The antenna exhibits a wide operating bandwidth achieved due to rectangular slits integrated into the Reuleaux-triangle patch. Meanwhile, adding rectangular slots in the ground plane improves the return loss level. Frequency reconfigurability is obtained by utilizing PIN diodes to adjust the current distribution, altering the antenna’s electrical length via the capacitive and inductive effects induced by the rings near the feed line. The antenna operates in two distinct frequency bands, 2.68–8.55 GHz and 12.7–15.65 GHZ, contingent upon the PIN-diodes’ ON/OFF states. In the OFF state, the antenna covers the UWB region, in particular, the ISM band (5.8 GHz), WLAN band (5.2 GHz), and lower X-band (8 GHz), exhibiting a 10 dB impedance bandwidth from 2.68 to 8.55 GHz with a maximum gain of 2.36 dBi. In the ON state, the antenna functions in the Ku band (12.7–15.65 GHz) with gains from 2.63 to 3.85 dBi. The antenna’s dynamic switching between UWB and Ku band operations makes it suitable for applications such as satellite communications, health monitoring, 5G, aerospace, and remote sensing.

Enhanced object detection in low-visibility haze conditions with YOLOv9s

Low-visibility haze environments, marked by their inherent low contrast and high brightness, present a formidable challenge to the precision and robustness of conventional object detection algorithms. This paper introduces an enhanced object detection framework for YOLOv9s tailored for low-visibility haze conditions, capitalizing on the merits of contrastive learning for optimizing local feature details, as well as the benefits of multiscale attention mechanisms and dynamic focusing mechanisms for achieving real-time global quality optimization. Specifically, the framework incorporates Patchwise Contrastive Learning to fortify the correlation among positive samples within image patches, effectively reducing negative sample interference and enhancing the model’s capability to discern subtle local features of haze-impacted images. Additionally, the integration of Efficient Multi-Scale Attention and the Wise-IoU Dynamic Focusing Mechanism enhances the algorithm’s sensitivity to channel, spatial orientation, and locational information. Furthermore, the implementation of a nonmonotonic strategy for dynamically adjusting the loss function weights significantly boosts the model’s detection precision and training efficiency. Comprehensive experimental evaluations of the COCO2017 fog-augmented dataset indicate that the proposed algorithm surpasses current state-of-the-art techniques in various assessment metrics, including precision, recall, and mean average precision (mAP). Our source code is available at: https://github.com/PaTinLei/EOD.

Phytochemical variation, phenolic compounds and antioxidant activity of wild populations of Iranian oak

SARS-CoV-2 Omicron subvariant genomic variation associations with immune evasion in Northern California: A retrospective cohort study

Background The possibility of association between SARS-CoV-2 genomic variation and immune evasion is not known among persons with Omicron variant SARS-CoV-2 infection. Methods In a retrospective cohort, using Poisson regression adjusting for sociodemographic variables and month of infection, we examined associations between individual non-lineage defining mutations and SARS-CoV-2 immunity status, defined as a) no prior recorded infection, b) not vaccinated but with at least one prior recorded infection, c) complete primary series vaccination, and/or d) primary series vaccination and ≥1 booster. We identified all non-synonymous single nucleotide polymorphisms (SNPs), insertions and deletions in SARS-CoV-2 genomes with ≥5% allelic frequency and population frequency of ≥5% and ≤95%. We also examined correlations between the presence of SNPs with each other, with subvariants, and over time. Results Seventy-nine mutations met inclusion criteria. Among 15,566 persons infected with Omicron SARS-CoV-2, 1,825 (12%) were unvaccinated with no prior recorded infection, 360 (2%) were unvaccinated with a recorded prior infection, 13,381 (86%) had a complete primary series vaccination, and 9,172 (58%) had at least one booster. After examining correlation between SNPs, 79 individual non-lineage defining mutations were organized into 38 groups. After correction for multiple testing, no individual SNPs or SNP groups were significantly associated with immunity status levels. Conclusions Genomic variation identified within SARS-CoV-2 Omicron specimens was not significantly associated with immunity status, suggesting that contribution of non-lineage defining SNPs to immune evasion is minimal. Larger-scale surveillance of SARS-CoV-2 genomes linked with clinical data can help provide information to inform future vaccine development.

SMILES-based QSAR and molecular docking studies of chalcone analogues as potential anti-colon cancer

Abstract QSAR modeling was applied to predict the anti-colon activity (against HT-29) of 193 chalcone derivatives using the Monte Carlo method, based on the index of ideality correlation (IIC) target function. The models were constructed using CORAL software, which employed optimal descriptors combining SMILES notation and hydrogen-suppressed molecular graphs (HSG). Among the developed models, Split #2 was identified as the best-performing model, with R2_validation = 0.90, IIC_validation = 0.81, and Q2_validation = 0.89. The mechanistic interpretation of the models, utilizing enhancing/reducing promoters, demonstrated that the models are capable of accurately predicting the pIC50 values of other chalcone derivatives with high robustness and precision. Based on these promoters, ten new compounds were selected from the ChEMBL database for pIC50 prediction, and molecular docking was performed using the protein with PDB ID:1SA0.