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Carbon efficient quantum AI: an empirical study of ansätz design trade-offs in QNN and QLSTM models
Sustained illness burden over time among Australians with myalgic encephalomyelitis/chronic fatigue syndrome
Background Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a disabling chronic illness. Many people with ME/CFS (pwME/CFS) are unable to continue employment and require support to complete activities of daily living. Despite this, ME/CFS remains unrecognised as a disability in Australia. The present study aimed to highlight the profound burdens experienced by pwME/CFS over time to provide evidence of permanency and necessitate reforms to Australian healthcare policies. Methods Data were collected for this longitudinal investigation between 1 st October 2021 and 3 rd October 2024. All participants were Australian residents aged between 18 and 65 years fulfilling the Canadian or International Consensus Criteria. Sociodemographic information, medical history, illness presentation and patient-reported outcomes were collected using three self-administered questionnaires distributed at approximately six-month intervals. Illness presentation and patient-reported outcomes were investigated over 12 months with Cochran’s Q , Friedman and one-way repeated measures ANOVA tests using Statistical Package for the Social Sciences version 29.0. Quality of life data were compared with Australian population norms using one-sample Wilcoxon signed-rank tests. Results Thirty-two pwME/CFS (n = 22/32, 68.8% female) participated at all three time points. At baseline, the mean age was 44.03 years and median illness duration was 12.50 years. Participants reported a median of 30 symptoms at each time point — the most common of which were also the most severe in presentation. Importantly, there were no significant changes in any symptom or patient-reported outcome over the 12-month study period. Overall health status, physical health and the ability to participate in daily and work life activities were the most substantially impacted. Quality of life was significantly reduced among pwME/CFS when compared with population norms at all time points. Conclusions PwME/CFS face substantial and sustained illness burdens. These consistent, profound impairments emphasise the need for improved access to disability and social support services for pwME/CFS in Australia through policy reform.
Association of blood miR-15a, miR-146a and miR-200b levels with stages of diabetic retinopathy
Hybrid grey Wolf–Cuckoo search optimized linear quadratic regulator for robust quadrotor control
Green synthesis of zinc oxide nanoparticles using ethanolic leaf extract of Olea europaea and its in vitro evaluation on MDA-MB-231 cancer cell lines, antibacterial and antioxidant activities
There are about 1.4 million cases of breast cancer in women that are diagnosed annually worldwide. Treatments to downregulate these tumor cells include surgery, immunotherapy, and chemotherapy. This study evaluates the synthesis of zinc oxide nanoparticles (ZnONPs) and Olea europaea ( O. europaea ) and its characterization, antioxidant, antimicrobial, and cytotoxic effects. The Gas Chromatography-Mass Spectrometry analysis indicated bioactive compounds that are present in O. europaea, such as Apigenin, Oleoside, Hydroxytyrosol, Rutin, Oleuropein aglycone, Tyrosol, and Oleuropein. The Ultraviolet visible spectroscopy analysis indicated the spectrum with peaks of 238 nm, 282 nm, and 313 nm. The zeta sizing analysis showed a size of 86 nm with a charge of −12.64 mV, X-RAY Diffraction (XRD), which shows the crystalline structure of the material, and the Fourier Transform Infrared Spectrophotometer (FTIR), indicating the chemical bond, molecular composition, and the functional group. Scanning Electron Microscopy (SEM) and SEM-EDS (Energy Dispersive X-ray analysis were done to determine surface morphology and elemental composition. An antioxidant activity assay was performed using diphenylpicryl hydrazine (DPPH) for free radical scavenging activity. The antibacterial inhibition assay was performed, and the results obtained were gentamycin 42.55 mm, Pseudomonas aeruginosa (P. aeruginosa) 29.22 mm, Escherichia coli (E. coli) 12.55 mm , Staphylococcus aureus (S. aureus) 16.40 mm , and Bacillus cereus (B. cereus) 8.44 mm. Cytotoxicity assay was performed on Dulbecco’s Modified Eagle Medium DMEM-grown MDA-MB-231 cells with varying dosage concentrations. The result showed that after 24 hours of treatment, cells were reduced to 60%, and after 48 hours of incubation, there was a 47% effect of ZnONPs. ZnO nanoparticle activity caused the MDA-MB-231 breast cancer cells to shrink, aggregate, deform, and proliferate more slowly. This research showed the medicinal potency of O. europaea .
Causal machine learning uncovers conditions for convective intensification driven by organic and sulfate aerosols
Abstract Aerosols are often hypothesized to invigorate deep convective clouds (DCCs), but observational evidence remains limited and inconclusive. Clarifying this hypothesis is critical for regions vulnerable to thunderstorms and flooding, particularly highly polluted coastal cities. Leveraging a novel causal discovery–inference pipeline and high-resolution observations near Houston, TX, we identify multiple causal pathways among aerosols (mostly organic and sulfate), DCCs, and meteorological factors. However, a direct causal link from aerosols to DCCs is found to be uncommon, occurring in less than 35% of analyzed scenarios, and is characterized by strong conditionality and nonlinearity. When aerosol impacts on DCCs do occur, they can be substantial, enhancing DCC core heights by approximately 1.7 km, with 92% of this effect concentrated in warmer-phase cloud regions. Notably, the presence of sea breezes and the inclusion of all measured aerosol particles each enhance DCCs in over 95% of aerosol-sensitive cases.
Intrusion detection using search-based learning optimized ensemble tree classifier model
An Intrusion Detection System (IDS) is an important component of cybersecurity, meant to monitor malicious behaviour, detect, and respond to unauthorized activities in computer systems or networks. Generally, Intrusion detection (IDS) is classified into host-based IDS (HIDS) and network-based IDS (NIDS), which monitor individual devices and network traffic, respectively. Existing models faced certain limitations, including the dilemma of balancing false positives against false negatives, the challenge of adjusting to evolving threats, handling issues with high-dimensional information and encrypted traffic, and limited resource competence when dealing with privacy concerns. The proposed research work currently aims at developing an intrusion detection system that is more adaptive and effective to hinder these existing challenges and improve the security of digital environments. The study is related to applying an elaborate Search-based learning-optimized ensemble tree classifier (SBO-based ensemble tree classifier) for improving ID in Vehicular Ad Hoc Networks (VANETs). The ensemble classifier incorporates decision tree, random forest, extra tree, and eXtreme Gradient Boosting (XG Boost) classifiers, which are fused to provide a comprehensive interpretation of potential attacks within the VANET environment. Moreover, the research is enriched by incorporating Search-based learning optimization that takes advantage of their collective and adaptive nature. This innovative amalgamation attempts to perfect the aggregated response generated by the ensemble classifier, which fine-tunes the proposed model for effective intrusion detection. To facilitate the multi-dimensional orientation, four separate outputs, such as alpha, beta, gamma, and delta, were introduced, which allow the categorization of intrusion attacks based on specific types. More specifically, the experimental results illustrate that the proposed SBO-based ensemble tree classifier achieved superior performance with an accuracy of 96.56%, F1-score of 96.63%, FPR of 0.97, MCC of 0.97, Precision of 96.59%, Sensitivity of 96.68%, and Specificity of 96.52% for intrusion detection and outperforms the other existing methods using the BOT-IOT Dataset.
Effectiveness of an educational intervention based on the common-sense model of self-regulation for lung cancer patients after thoracoscopic surgery
Correction: Network analysis and site selection for cross-disciplinary collaboration: The Plant and Environmental Science Building at Michigan State University
Correlation between structural features and optoelectronic properties in superhalogen doped hexaazakekulenes
Spatiotemporal forelimb muscle activation during precise asymmetric stepping in rats
A sequence of muscle actions generates complex movements such as walking or reaching. However, how these coordinated actions subserve complex movements across animals remains unknown. While the sequences of muscle activity have been documented in limb tasks with large animals, the equivalent comprehensive behavioral description of rodent performance is sparse. To this end, we have trained rats to perform precise foot placement, which allows us to assess skilled limb placement during locomotion. Animals were trained on the pegway task, conFigd to impose symmetric or asymmetric (with overstepping) locomotor stepping at the preferred stride length. We collected electromyography from selected representative forelimb muscles implanted with intramuscular differential electrodes and recorded ground reaction forces from the array of force sensors embedded into walkway pegs. The changes in muscle coordination were analyzed for symmetric and asymmetric stepping. The sequence corresponded to the progression of muscle actions responsible for limb lift, flexion and transport, overground clearance, and preparation for ground contact. The stereotyped spatiotemporal sequence of muscle activity was persistent and consistent across asymmetric tasks. These patterns are similar to those observed in cats during locomotion over obstacles and reaching movements. These findings indicate that a temporal sequence of muscle actions is similar across quadrupeds during locomotor tasks with fine stepping control.
The temporal characteristics of pharyngeal microbiota and plasma lipidomics by silica exposure in coal miner
Long-term outcomes following drug-coated balloons versus thin-strut drug-eluting stents for treatment of in-stent restenosis in Chronic Kidney Disease (CKD Dragon-Registry)
We sought to investigated the outcomes of patients with chronic kidney disease (CKD) and drug-eluting stent (DES)-in-stent restenosis (ISR) undergoing percutaneous coronary intervention (PCI) with a drug-coated balloon (DCB) or thin strut drug-eluting stent (thin-DES). Consecutive patients with DES-ISR who underwent PCI with a thin-DES or a paclitaxel-coated DCB for DES-ISR were enrolled. The primary outcome was target lesion revascularization (TLR), while the secondary was target vessel revascularization (TVR) and device-oriented composite endpoint (DOCE). The pooled analysis included 1,317 patients, with 585 (44.42%) treated using a thin-DES and 732 (55.58%) by DCB. In the crude analysis of CKD patients (n = 286) undergoing PCI for ISR, thin-DES vs. DCB showed similar outcomes for TLR (hazard ratio [HR]=0.94, 95% confidence interval [CI]=0.44–2.00; p = 0.873), TVR (HR = 0.82, 95% CI = 0.44–1.55; p = 0.542), MI (HR = 0.71, 95% CI = 0.34–1.46; p = 0.348) and DOCE (HR = 0.71, 95% CI = 0.36–1.40; p = 0.325). After propensity score matching (n = 184), the HRs remained non-significant for TLR (0.52, 95% CI = 0.21–1.29; p = 0.159), TVR (0.54, 95% CI = 0.24–1.01; p = 0.134), MI (0.56, 95% CI = 0.24–1.32; p = 0.183), TV-MI (0.56, 95% CI = 0.09–3.39; p = 0.528), cardiac death (0.63, 95% CI = 0.10–3.81; p = 0.615), and DOCE (0.45, 95% CI = 0.19–1.04; p = 0.062). In conclusion, in CKD patients undergoing PCI for ISR, thin‐DES treatment was associated with a numerical reduction in TLR, TVR, and DOCE compared with DCB. However, these differences did not achieve statistical significance in the crude or propensity score-matched analyses.
Calibration-free sEMG intention recognition via self-supervised pretraining and adversarial domain alignment for upper-limb rehabilitation
Potential FSH-mediated molecular pathway to regulate follicle development in striped hamsters (Cricetulus barabensis) supported by strong correlative evidence
Scientific background Rational control of rodent populations is crucial for maintaining ecosystem balance and mitigating agricultural economic losses. Follicle development plays a pivotal role in determining animal population abundance, and photoperiod serves as the primary environmental cue affecting this process. Investigating the mechanisms through which photoperiod influences follicle development in the striped hamster ( Cricetulus barabensis ) offers a promising molecular target for the effective and sustainable management of rodent populations. Methodology This study employed hematoxylin and eosin (HE) staining to evaluate ovarian developmental status under different photoperiods, including quantification of follicles at various developmental stages and the number and thickness of granulosa cell layer, thereby elucidating the effects of photoperiod on follicle development. Subsequently, enzyme-linked immunosorbent assay (ELISA) was used to measure serum FSH and fecal E2 concentrations, while real-time quantitative PCR was performed to determine mRNA levels of CCND1 and CCND2 . Correlation analyses between these markers and follicle counts were conducted to identify key factors involved in follicle development. Furthermore, both real-time quantitative PCR and Western blotting were utilized to investigate the expression of transcription factors FOXO1, FOXL2, and NR5A2 in the ovary at the mRNA and protein levels, respectively, and their relationships with follicle numbers were analyzed, to reveal the potential molecular pathways through which photoperiod regulates follicle development in the striped hamster. Results The results demonstrate that LP enhances the synthesis of FSH, promotes granulosa cell proliferation, and stimulates follicle development, whereas SP exerts an opposing effect in the striped hamster. FSH is a key hormone involved in follicle development regulated by photoperiods, and CCND2 influences follicle development by modulating granulosa cell proliferation. Additionally, photoperiod alters the expression levels of transcription factors FOXO1, FOXL2, and NR5A2. Correlation analyses revealed that serum FSH concentration was significantly positively correlated with the expression levels of FOXO1 and FOXL2. In turn, the expression of FOXO1 and FOXL2 was significantly positively associated with that of NR5A2, which also showed a significant positive correlation with CCND2 expression. These results suggest a potential regulatory pathway—FSH-FOX-NR5A2-CCND2—involved in photoperiod-dependent follicle development in the striped hamster. Conclusion The FSH-FOX-NR5A2-CCND2 pathway represents a potential molecular mechanism by which photoperiod regulates follicle development, supported by robust correlative evidence in the striped hamster. The transcription factors FOXO1, FOXL2, and NR5A2 are identified as candidate targets of reproductive activity, with NR5A2 showing a stronger correlation than FOXO1 and FOXL2, thus providing a theoretical foundation for the rational control of rodent population dynamics.
Pilot study on exercise-induced placental transcriptomic changes and oxidative stress reduction in gestational diabetes mellitus
Abstract Gestational diabetes mellitus (GDM) is a common pregnancy complication associated with adverse maternal and neonatal outcomes, characterised by inflammation and oxidative stress. While exercise interventions have been shown to alleviate some of these issues, the underlying molecular mechanisms, particularly in the placenta, remain poorly understood. This study investigates the impact of exercise on maternal immune function, oxidative stress, placental gene expression, and neonatal outcomes in GDM pregnancies. This pilot study involved 12 pregnant women, six with GDM and six with normal pregnancies. Participants were divided into four groups: normal pregnancies with exercise (NCE) or without exercise (NC), and GDM pregnancies with exercise (GDME) or without exercise (GDM). The exercise intervention included stationary cycling for 16 weeks, three times a week. Placental tissue and maternal blood were collected post-delivery. Placental gene expression was analysed using RNA sequencing, and oxidative stress was measured in maternal blood. Neonatal birth weight was significantly lower in the GDME group compared to the GDM group. Exercise significantly reduced oxidative stress and improved immune function in the GDME group, approaching levels observed in the NC and NCE groups. Transcriptomic analysis of placental samples revealed upregulation of antioxidant genes (e.g., GPX3, MTCO1P40 ) and downregulation of pro-inflammatory genes (e.g., CCL21 ), body weight regulatory gene ( IGFBP1 ), indicating enhanced immune and metabolic balance, and regulation of fetal birth weight. Exercise interventions in GDM pregnancies improve placental function by modulating immune response and oxidative stress, improving neonatal outcomes. These findings support the inclusion of exercise in GDM management to optimise maternal and fetal health.
Development of an automated ultrasonographic detection method for fecal retention using a transgluteal cleft approach
This study aimed to develop an artificial intelligence–based classification system using ultrasound images obtained via a transgluteal cleft scanning approach for detecting fecal retention in the lower rectum. The goal was to support accurate, objective constipation assessment by nurses in home care settings, where traditional diagnostic tools are often unavailable. Ultrasound videos of the lower rectum were collected from 24 patients undergoing dialysis at a mixed-care hospital. From 90 videos, 2,855 still images were extracted and labeled by expert sonographers based on the presence or absence of hyperechoic areas indicating fecal retention. A deep learning segmentation model using U-Net with a ResNeXt-50 encoder was trained and evaluated. Performance was measured using the intersection over union threshold of 0.5 to define true positives. Accuracy, sensitivity, and specificity were calculated on a test dataset of 758 images. Among the test images, 376 (49.6%) showed fecal retention. The AI system achieved a sensitivity of 81.6%, specificity of 84.0%, and overall accuracy of 82.8%. The mean IoU was 0.601 ± 0.185, indicating a high level of agreement between expert annotations and AI-generated predictions. The tool reliably detected fecal retention in ultrasound images obtained using the transgluteal cleft approach, which overcomes limitations of traditional transabdominal scanning caused by obesity, bladder emptying, or bowel gas. The proposed AI-assisted ultrasound system showed high diagnostic performance in identifying fecal retention in the lower rectum. It may enable non-specialist nurses to assess constipation more safely and accurately, particularly in home-care environments. This technology has the potential to reduce unnecessary laxative use and invasive interventions, ultimately improving the quality of bowel care for older adults with impaired communication or mobility.
Reinforcement learning based dynamic vegetation index formulation for rice crop stress detection using satellite and mobile imagery
To test or not to test? Study protocol for a best-worst scaling to understand decision-making and preferences for genetic testing in moderate-risk individuals
Introduction Genetic testing is usually offered to individuals at high risk of carrying disease-causing variants. For those at moderate risk of genetic conditions, testing could also help in early detection, prevention, and treatment. Although individuals’ preferences to undergo genetic testing can influence their treatment decisions, there is limited research on preferences of moderate-risk individuals. This study aims to estimate the relative importance of factors that influence decision-making for genetic testing of moderate-risk individuals from different disease cohorts and testing types. Methods We outline the study protocol for a best-worst scaling (BWS) object case (Case 1) and a ranking exercise around primary genetic testing and secondary analyses, respectively. Individuals (n = 350) at moderate risk of breast cancer or aortic disease will be recruited through genetic clinics who are part of PreventGene to complete an online preferences survey after deciding whether to have genetic testing, but before receiving the test results. Thirteen BWS items were selected based on the results of a scoping review and input from clinical experts. A balanced incomplete block design will be used. Respondents are asked to select the most (best) and least (worst) important factors in their decision-making. Data will be analysed using count analysis, multinomial logit, and latent class analyses. The data collection started in March 2025 and is expected to be finished by spring 2026. Discussion Understanding how individuals at moderate risk make genetic testing decisions can help to better understand the decision-making process about what testing types should be available in which contexts and for which individuals. Findings can inform clinical and health policy decision-makers in planning and offering additional future genetic testing programs for moderate-risk individuals. The study is registered in the Open Science Framework (10.17605/OSF.IO/JFPH9).