Browse Articles

RS-SCBiGRU: a noise-robust neural network for high-speed motor fault diagnosis with limited samples

Abstract Convolutional Neural Networks, with their excellent capabilities for automatic feature discrimination and learning, have been widely applied in the field of mechanical fault diagnosis. However, in real-world operating environments, acquiring large amounts of fault data as training samples is often challenging, which limits the applicability of traditional methods. To address this issue, this study proposes a frequency-adaptive fault diagnosis method for high-speed motors under small-sample scenarios. Specifically, this paper designs an innovative data augmentation technique that effectively expands the diversity and coverage of the training dataset and is seamlessly integrated into the fault diagnosis model. Furthermore, to enhance the richness of feature representations and strengthen information exchange between different feature channels, this paper proposes a frequency-adaptive convolutional layer (SCNET), which significantly optimizes the performance of Bidirectional Gated Recurrent Units (BiGRU) in fault feature extraction. Based on these technological improvements, we have constructed an efficient intelligent fault diagnosis model named RS-SCBiGRU. Experimental validation shows that, compared to various advanced fault diagnosis methods, the RS-SCBiGRU model achieves a significant improvement in accuracy and demonstrates stronger noise resistance capabilities.

Amorphous/crystalline HTiNbO5-X membranes for efficient confined flow synthesis of acetate ester flavours

Multi-factor consideration sample assignment for oriented tiny object detection

A human activity recognition model based on deep neural network integrating attention mechanism

Immunogenicity and safety of CoronaVac vaccine in children and adolescents (Immunita-002, Brazil): A phase IV six-month follow up

Abstract Vaccines are essential for the prevention and control of several diseases, and monitoring the immune response generated by vaccines is crucial. The immune response generated by vaccination against SARS-CoV-2 in children and adolescents is not well defined in terms of the intensity and medium to long-term duration of protective immunity, which may indicate the need for booster doses and could support decisions in public health. The study aims to evaluate the immunogenicity and safety of an inactivated SARS-CoV-2 vaccine (CoronaVac) in a two-dose primary protocol in children and adolescents aged 3 to 17 years old in Brazil. Participants were invited to the research at two public healthcare centers located in Serrana (São Paulo) and Belo Horizonte (Minas Gerais), Brazil. They underwent medical interviews to gather their medical history, including COVID-19 history and medical records. Physical exams were conducted, which included measurements of weight, blood pressure, temperature, and pulse rate. Blood samples were obtained from the participants before vaccination, 1 month after the first dose, and at 1, 3, and 6 months after the second dose. These samples were followed up using a virtual platform to monitor post-vaccination reactions and symptoms of COVID-19. The SARS-CoV-2 genome from swab samples of COVID-19 positive individuals was sequenced using NGS. Total antibodies were measured by ELISA, and neutralizing antibodies to the B.1 lineage and Omicron variant (BA.1) were quantified by PRNT and VNT assays. The cellular immune response was evaluated by flow cytometry through the quantification of systemic soluble immune mediators. The follow-up of 640 participants showed that CoronaVac was able to significantly induce the production of total IgG antibodies to SARS-CoV-2 and the production of neutralizing antibodies to the B.1 lineage and Omicron variant. Additionally, a robust cellular immune response was observed, characterized by a wide release of pro-inflammatory and regulatory mediators in the early post-immunization moments. Adverse events recorded so far have been mild and transient, except for seven serious adverse events reported on VigiMed. The results indicate a robust and sustained immune response induced by CoronaVac in children and adolescents for up to six months, providing evidence to support the safety and immunogenicity of this effective immunizer.

Human and ecological health risks from heavy metal contamination in groundwater aquifers

Source apportionment and dynamics of PM2.5 across regions during and after the coronavirus 2019 pandemic

Resolving hyperelasticity-adhesiveness conflict in polymer networks by in situ constructing mechanical heterogeneities

Socioeconomic inequalities in disability prevalence and health service use in Bangladesh

In vitro antiviral activities of thymol and Limonin against influenza a viruses and SARS-CoV-2

Impact of smoking and opium cessation on gastrointestinal cancer risk: A 15-year longitudinal study in Golestan Cohort

Infrared thermography based seepage and erosion detection in earthen dams using laboratory scale models

Direct recording of electrically evoked cortical potentials from cochlear implants demonstrates feasibility and clinical relevance in pediatric users

Deep molecular profiling of synovial biopsies in the STRAP trial identifies signatures predictive of treatment response to biologic therapies in rheumatoid arthritis

Abstract Approximately 40% of patients with rheumatoid arthritis do not respond to individual biologic therapies, while biomarkers predictive of treatment response are lacking. Here we analyse RNA-sequencing (RNA-Seq) of pre-treatment synovial tissue from the biopsy-based, precision-medicine STRAP trial (n = 208), to identify gene response signatures to the randomised therapies: etanercept (TNF-inhibitor), tocilizumab (interleukin-6 receptor inhibitor) and rituximab (anti-CD20 B-cell depleting antibody). Machine learning models applied to RNA-Seq predict clinical response to etanercept, tocilizumab and rituximab at the 16-week primary endpoint with area under receiver operating characteristic curve (AUC) values of 0.763, 0.748 and 0.754 respectively (n = 67-72) as determined by repeated nested cross-validation. Prediction models for tocilizumab and rituximab are validated in an independent cohort (R4RA): AUC 0.713 and 0.786 respectively (n = 65-68). Predictive signatures are converted for use with a custom synovium-specific 524-gene nCounter panel and retested on synovial biopsy RNA from STRAP patients, demonstrating accurate prediction of treatment response (AUC 0.82-0.87). The converted models are combined into a unified clinical decision algorithm that has the potential to transform future clinical practice by assisting the selection of biologic therapies.

Comparative study of five-year cervical cancer cause-specific survival prediction models based on SEER data

Abstract Cervical cancer (CC) is a major cause of mortality in women, with stagnant survival rates, highlighting the need for improved prognostic models. This study aims to develop and compare machine learning models for predicting five-year cause-specific survival (CSS) in CC patients and evaluate their performance against traditional methods like the Cox Proportional Hazards model. Using data from the Surveillance, Epidemiology, and End Results (SEER) program, we applied the Synthetic Minority Over-Sampling Technique to address class imbalance and used stepwise forward selection, feature importance, and permutation importance for feature selection. The Gradient Boosting Survival Analysis (GBSA) model outperformed others with an Inverse Probability of Censoring Weighted Concordance Index of 0.835 and an Integrated Brier Score of 0.120. SHAP value analysis identified tumor stage and surgical resection as key factors. These findings address a critical gap in CSS prediction for CC patients and offer insights for clinical decision-making and personalized treatment. The GBSA model provides more accurate survival predictions, aiding clinicians in tailoring treatment strategies to improve patient outcomes. However, the retrospective study design, potential SEER data entry errors, and the lack of genetic markers and detailed treatment protocols should be considered when interpreting the results.

Bacterioruberin extract from Haloferax mediterranei induces apoptosis and cell cycle arrest in myeloid leukaemia cell lines

Esophagectomy enhances hypertension remission and metabolism via weight loss in esophageal Cancer patients with hypertension

Short and long-term outcomes of children and adolescents hospitalized with COVID-19 or influenza: results of the AUTCOV study

Study on the distribution characteristics and seismic hazard evaluation of loess seismic landslides in the southern Ningxia area

Immuno-metabolic stress responses control longevity from mitochondrial translation inhibition in C. elegans

Abstract Perturbing mitochondrial translation represents a conserved longevity intervention, with proteostasis processes proposed to mediate the resulting lifespan extension. Here, we explore whether other mechanisms may contribute to lifespan extension upon mitochondrial translation inhibition. Using multi-omics and functional in vivo screening, we identify the ethylmalonyl-CoA decarboxylase orthologue C32E8.9 in C. elegans as an essential factor for longevity induced by mitochondrial translation inhibition. Reducing C32E8.9 completely abolishes lifespan extension from mitochondrial translation inhibition, while mitochondrial unfolded protein response activation remains unaffected. We show that C32E8.9 mediates immune responses and lipid remodeling, which play crucial roles in the observed lifespan extension. Mechanistically, sma-4 (a TGF-β co-transcription factor) serves as an effector of C32E8.9, responsible for the immune response triggered by mitochondrial translation inhibition. Collectively, these findings underline the importance of the “immuno-metabolic stress responses” in longevity upon mitochondrial translation inhibition and identify C32E8.9 as a central factor orchestrating these responses.