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A compact tri-band omnidirectional antenna design for CubeSat applications
SAD-Net: a full spectral self-attention detail enhancement network for single image dehazing
Development and validation of an LC–MS/MS method for quantitative determination of LXT-101 sustained-release suspension, a novel drug in treating prostate cancer, in beagle plasma
Physiological assessment of the psychological flow state using wearable devices
Abstract Flow is the state of optimal experience which can lead to outstanding performance. Our study demonstrates the feasibility of detecting and monitoring flow using wearable devices. Twenty-eight Hungarian adults participated in the experiment. They played a game at different levels to induce flow and anti-flow states, which was tested with questionnaires. We measured electroencephalography (EEG), heart rate (HR), blood oxygen saturation (SpO2) and galvanic skin response (GSR) signals as well as head and hand motion. We isolated EEG delta, theta, alpha and beta band power, HR, SpO2 and GSR average and standard deviation, as well as acceleration and angular velocity standard deviation. In flow condition, alpha and theta power were the dominant components, in accordance with the transient hypofrontality hypothesis. We also replicated the U-shaped characteristic of the heart rate variability; in addition, we propose an inverse U-shaped and a U-shaped characteristic for SpO2 and SpO2 variability, respectively. On the basis of motion tracking, subjects were the least physically active in flow, signifying a focused state, and the most active in boredom. Our results support the applicability of lightweight, wearable devices for monitoring mental state that can be utilized to improve well-being at work or in everyday situations.
Static magnetic field promotes the doxorubicin toxicity effects on osteosarcoma cells
Antiviral effect of Bromelain combined with acetylcysteine against SARS-CoV-2 Omicron variant
The impacts of expertise, conflict, and scientific literacy on trust and belief in scientific disagreements
Abstract Media portrayals of scientific disagreements can blur distinctions between experts and non-experts, or include disagreements from vested individuals, potentially undermining trust in science and belief in scientific claims. We investigated how disagreeing sources’ expertise and conflicting interests impact trust in scientific experts and belief in their claims, and whether scientific literacy moderates these effects. Across three, 2 × 2 factorial experiments with a student (N = 105) online (N = 110), and general Australian sample (N = 105), participants read articles describing a scientific claim followed by a disagreeing source whose subject-matter expertise (present/absent) and vested interest (present/absent) were manipulated. Participants in all samples judged the original scientific expert as more trustworthy and their claims more believable when the disagreeing source lacked relevant subject-matter expertise. Among student participants, conflicts of interest also impacted belief in scientific claims (but not trust in the scientist), and scientific literacy enhanced sensitivity to expertise and conflict, however, the other samples were largely insensitive to vested interests, and scientific literacy had varied effects in these samples. Our results show disagreement in the news, even from questionable sources, can sway evaluations of scientific claims and scientists, and highlight the value of literacy-based interventions in science communication.
Transformer-based deep learning ensemble framework predicts autism spectrum disorder using health administrative and birth registry data
Abstract Early diagnosis and access to resources, support and therapy are critical for improving long-term outcomes for children with autism spectrum disorder (ASD). ASD is typically detected using a case-finding approach based on symptoms and family history, resulting in many delayed or missed diagnoses. While population-based screening would be ideal for early identification, available screening tools have limited accuracy. This study aims to determine whether machine learning models applied to health administrative and birth registry data can identify young children (aged 18 months to 5 years) who are at increased likelihood of developing ASD. We assembled the study cohort using individually linked maternal-newborn data from the Better Outcomes Registry and Network (BORN) Ontario database. The cohort included all live births in Ontario, Canada between April 1st, 2006, and March 31st, 2018, linked to datasets from Newborn Screening Ontario (NSO), Prenatal Screening Ontario (PSO), and Canadian Institute for Health Information (CIHI) (Discharge Abstract Database (DAD) and National Ambulatory Care Reporting System (NACRS)). The NSO and PSO datasets provided screening biomarker values and outcomes, while DAD and NACRS contained diagnosis codes and intervention codes for mothers and offspring. Extreme Gradient Boosting models and large-scale ensembled Transformer deep learning models were developed to predict ASD diagnosis between 18 and 60 months of age. Leveraging explainable artificial intelligence methods, we determined the impactful factors that contribute to increased likelihood of ASD at both an individual- and population-level. The final study cohort included 707,274 mother-offspring pairs, with 10,956 identified cases of ASD. The best-performing ensemble of Transformer models achieved an area under the receiver operating characteristic curve of 69.6% for predicting ASD diagnosis, a sensitivity of 70.9%, a specificity of 56.9%. We determine that our model can be used to identify an enriched pool of children with the greatest likelihood of developing ASD, demonstrating the feasibility of this approach.This study highlights the feasibility of employing machine learning models and routinely collected health data to systematically identify young children at high likelihood of developing ASD. Ensemble transformer models applied to health administrative and birth registry data offer a promising avenue for universal ASD screening. Such early detection enables targeted and formal assessment for timely diagnosis and early access to resources, support, or therapy.
Author Correction: Brain milieu induces early microglial maturation through the BAX-Notch axis
Iridium porphyrin-catalysed asymmetric carbene insertion into primary N-adjacent C–H bonds with TON over 1000000
Antisite defect unleashes catalytic potential in high-entropy intermetallics for oxygen reduction reaction
Lactobacillus acidophilus potentiates oncolytic virotherapy through modulating gut microbiota homeostasis in hepatocellular carcinoma
Tsirelson bounds for quantum correlations with indefinite causal order
Author Correction: Accelerating discovery of bioactive ligands with pharmacophore-informed generative models
Enabling new insights from old scans by repurposing clinical MRI archives for multiple sclerosis research
Abstract Magnetic resonance imaging (MRI) biomarkers are vital for multiple sclerosis (MS) clinical research and trials but quantifying them requires multi-contrast protocols and limits the use of abundant single-contrast hospital archives. We developed MindGlide, a deep learning model to extract brain region and white matter lesion volumes from any single MRI contrast. We trained MindGlide on 4247 brain MRI scans from 2934 MS patients across 592 scanners, and externally validated it using 14,952 scans from 1,001 patients in two clinical trials (primary-progressive MS and secondary-progressive MS trials) and a routine-care MS dataset. The model outperformed two state-of-the-art models when tested against expert-labelled lesion volumes. In clinical trials, MindGlide detected treatment effects on T2-lesion accrual and cortical and deep grey matter volume loss. In routine-care data, T2-lesion volume increased with moderate-efficacy treatment but remained stable with high-efficacy treatment. MindGlide uniquely enables quantitative analysis of archival single-contrast MRIs, unlocking insights from untapped hospital datasets.
Enantioselective acyl-trifluoromethylation of olefins by bulky thiazolium carbene catalysis
Abstract Enantioenriched α-chiral β-fluorinated ketones are valuable structural motifs with application in several fields. The recently emerged concept of NHC-catalyzed radical acyl-trifluoromethylation of olefins offers a rapid route to construct racemic β-fluorinated ketones in a single step. Due to the lack of competent chiral NHC catalysts constructing these molecules in an enantioselective manner remains an unmet challenge. Herein, we report a family of chiral thiazolium carbenes having bulky chiral flanking groups and offering three distinct positions with broad steric and electronic tunability. The catalysts display so far unmatched enantioselectivities for acyl-trifluoromethylations of simple unactivated olefins with a wide variety of aldehydes and Togni’s reagent. The method provides a variety of enantioenriched β-trifluoromethylated α-chiral ketones in high yields and excellent enantioselectivities up to 98:2 er. A potential applicability of this methodology is demonstrated through enantio- and diastereoselective late-stage functionalizations of pharmaceutical compounds.
dFLASH; dual FLuorescent transcription factor activity sensor for histone integrated live-cell reporting and high-content screening
Abstract Live-cell transcription factor (TF) activity reporting is crucial for synthetic biology, drug discovery and functional genomics. Here we present dFLASH (dual FLuorescent transcription factor Activity Sensor for Histone-integrated live-cell reporting), a modular, genome-integrated TF sensor. dFLASH homogeneously and specifically detects endogenous Hypoxia Inducible Factor (HIF) and Progesterone Receptor (PGR) activities, as well as coactivator recruitment to synthetic TFs. The dFLASH system produces dual-color nuclear fluorescence, enabling normalized, dynamic, live-cell TF activity sensing with strong signal-to-noise ratios and robust screening performance ( Z ’ = 0.61–0.74). We validate dFLASH for functional genomics and drug screening, demonstrating HIF regulation via CRISPRoff and application to whole-genome CRISPR KO screening. Additionally, we apply dFLASH for drug discovery, identifying HIF pathway modulators from a 1600-compound natural product library using high-content imaging. Together, this versatile platform provides a powerful tool for studying TF activity across diverse applications.