Browse Articles

Behaviour and quantitative source analysis of heavy metals in pore water profiles at the Inlet of Dongting lake from the Xiangjiang river

Experimental diagenesis reveals preservation of biosignatures in filamentous sulfur mats under hydrothermal conditions

Abstract Filamentous microbial biosignatures associated with iron sulfides are among the prime targets in early life studies, but their formation and preservation are insufficiently understood. Here, we experimentally evaluated the taphonomy of filamentous sulfur-oxidizing bacteria exposed to iron–sulfur–rich conditions and high temperatures (≤ 80 °C), mimicking burial diagenesis and/or hydrothermal alteration. The addition of ferrihydrite and sulfide at 22 °C resulted in a near-instantaneous formation of iron sulfides. Heating to 80 °C for 2–6 weeks resulted in the formation of polysulfides and magnetic Fe- and/or S-containing minerals, with low pyritization (~ 11%). Notably, Fe–S mineral formation was only loosely associated with the filaments. However, intracellular elemental sulfur released from the sulfur-oxidizing bacteria re-precipitated extracellularly, coating individual filaments, possibly promoting the formation of pyritic crusts during later diagenetic stages. Taken together, our study revealed that biosignatures in filamentous sulfur mats might be preserved in a variety of environments, including hydrothermal systems on and beyond the Earth.

Evaluation of non-indigenous biological assessment tools using benthic macroinvertebrates in a regulated river in the semi-arid region of Iran

Evaluation of color matching of single-shade resin composites with different background shades: an in vitro study

Prediction of irritable bowel syndrome by integrating urine metabolites and gut microbiota

Abstract Irritable bowel syndrome (IBS) is a functional gastrointestinal disorder characterized by recurrent abdominal pain and altered bowel habits. While gut microbiota alterations and metabolic disturbances have been implicated in IBS, their potential role in classification and patient stratification remains unclear. This study aimed to evaluate the potential of integrating gut microbiota profiling and urinary metabolomics for improved IBS classification. Fifty-four participants (27 healthy controls, 27 IBS patients) were recruited for gut microbiota and urinary metabolite analysis. Gut microbiota composition was assessed via 16S rRNA gene sequencing, and urinary metabolites were profiled using gas chromatography-mass spectrometry (GC-MS). Receiver operating characteristic (ROC) curve analysis was performed to assess the predictive accuracy of gut microbiota, urinary metabolites, and their combined model. LEfSe analysis identified that Clostridia and Prevotella as enriched in IBS patients, whereas Bacteroidales and Faecalitalea were predominant in healthy individuals. Urinary metabolite analysis revealed significant alterations in metabolite profiles, with IBS patients exhibiting elevated fructose levels and trends of increased serine, mannose, and galactose. ROC curve analysis demonstrated that urinary metabolomics (AUC = 0.65) outperformed gut microbiota profiling (AUC = 0.54), while a combined approach integrating both datasets achieved the highest predictive accuracy (AUC = 0.74). These findings indicate that integrating urinary metabolomics with gut microbiota profiling may provide preliminary insights into IBS classification. Given the small sample size, potential risk of overfitting, absence of external validation, and possible dietary or medication confounding, the observed performance of the combined approach should be regarded as hypothesis-generating rather than confirmatory. Nevertheless, the results highlight the potential utility of integrative omics strategies in IBS research, underscoring the need for validation in larger, sex-balanced, and clinically diverse cohorts to establish their robustness and broader relevance.

A multiplex real-time PCR assay for detection of equid herpesvirus 1 and 4

The role of amygdala reactivity in affective fluctuations across social contexts

Abstract The amygdala plays a critical role in socio-emotional processing, serving not only as a key neural substrate for shaping emotional experiences and social behavior, but also as a trait-like individual risk factor that confers heightened vulnerability to emotional disorders, including anxiety and depression. While prior research has primarily examined the link between heightened amygdala reactivity and negative affect (NA), much less is known about its relationship with positive affect (PA), particularly across different social contexts. In this study, we focused on how these associations vary based on the level of social intimacy and distinct facets of affect (e.g., high vs. low-arousal PA). Using a combined fMRI and ecological momentary assessment (EMA) approach, we examined how individual differences in amygdala reactivity relate to momentary emotional experiences across diverse social contexts in daily life. As expected, interactions with close companions had a robust mood-enhancing effect. Notably, individual differences in amygdala reactivity moderated the association between social context and high-arousal PA, such that individuals with heightened amygdala reactivity reported lower levels of high-arousal PA when alone, compared to when they were with close others. These findings highlight the specific social contexts and affective states most relevant to individual differences in amygdala reactivity, offering novel insights into the dynamic interplay between brain function, emotional experiences, and social contexts.

Integrating event information and multi dimensional relationships for improved financial time series forecasting

Abstract Financial time series prediction is extremely challenging due to the intertwined effects of market narratives and complex inter-asset relationships. Traditional prediction models often fail to distinguish similar price patterns driven by different underlying causes, limiting their predictive accuracy in practical scenarios. To address these limitations, this study proposes the Dual-stream Alpha Factor Fusion Network (DAFF-Net), an innovative deep learning framework that integrates event-driven temporal pattern extraction with multi-dimensional relationship-aware channel soft clustering. The event-driven temporal pattern extractor employs an event-aware router to fuse time series data with contextual event information encoded from news, corporate announcements, and macroeconomic data, enabling the model to understand the underlying narratives behind market fluctuations. The multi-dimensional relationship-aware channel soft clustering module constructs a comprehensive asset relationship network through adaptive fusion of frequency-domain, fundamental, and knowledge graph relationships, which is more effective than single-relationship approaches and better captures complex cross-asset dependencies. We validated our approach primarily on Amazon stock data covering the period from 2010 to 2025, with additional cross-asset validation on four stocks from different sectors (healthcare, financial, energy, and electric vehicle sectors). Results demonstrate that DAFF-Net significantly outperforms eight representative baseline models including ARIMA, LSTM, Transformer, and DUET across multiple prediction time horizons. Specifically, compared to the strongest baseline, DAFF-Net achieves 7.4%-15.2% improvement in MSE and 7.0%-21.4% enhancement in $$\text {R}^{2}$$ metrics, showing particularly outstanding advantages in long-term prediction tasks. These results prove the effectiveness of integrating event information and multi-dimensional relationships in financial prediction, providing a new technical paradigm for quantitative investment and risk management applications.

Melissa officinalis essential oil modulates oxidative balance, cholinergic activity, and cognitive performance in a scopolamine-induced zebrafish model: implications for neuroprotective strategies in cognitive disorders

Development of an evidence-based evaluation framework for digital health software products

Elucidating the mechanism by which Aloe-emodin from Cassiae semen treats diabetic retinopathy via network pharmacology and experimental verification

Molecular characterization of chronic inflammatory diseases of the urinary bladder based on next-generation RNA sequencing and digital image analysis

Enhancing yogurt health benefits with moringa and black seed oil nanoemulsions to improve fatty acids and microbial viability

Abstract The increasing consumer demand for natural functional foods has prompted the development of fortified dairy products with improved nutritional and health benefits. This study designed and evaluated yoghurts fortified with cold-pressed moringa seed oil (MSO) and black seed oil (BSO) at 1.5 g /L, as well as their water-based nanoemulsions at 3 g Nanoemulsion powder/L, compared to a plain control. Phenolic profiles characterized by HPLC-MS revealed high levels of ellagic acid (23.5 mg/100 g), rutin (18.2 mg/100 g), and chlorogenic acid (21.4 mg/100 g) in MSO, and chlorogenic acid (19.8 mg/100 g), apigenin (16.7 mg/100 g), and naringenin (14.3 mg/100 g) in BSO. Nanoemulsions with 5% oil showed droplet sizes of 69.1 nm (MSO) and 38.1 nm (BSO) and zeta potentials above − 30 mV, confirming good colloidal stability over 7 days. Cytotoxicity assays indicated a safe dose up to 80 µg/mL. Yoghurt fortified with nanoemulsions exhibited a significant increase in total solids (+ 12%), unsaturated fatty acids (notably omega-3 increased by 25%), and antioxidant capacity (DPPH radical scavenging improved by 30%) while reducing acidity and syneresis relative to the control. Lactic acid bacteria viability remained unaffected. Sensory evaluation showed improved color, texture, and overall acceptance for yoghurts with 1.5 g nanoemulsion addition per liter. These findings demonstrate that incorporation of MSO and BSO nanoemulsions at this level effectively enhances yoghurt’s nutritional and functional properties without compromising microbial or sensory quality.

Sorghum husks as potential low cost adsorbent for Congo red adsorption

Seeding Alzheimer’s disease-associated tau pathology in MAPT knock-in primary neurons causes early axonopathy and synaptic dysfunction

Abstract The progressive accumulation of pathological tau is a hallmark of Alzheimer’s disease (AD). The bulk of existing in vitro and in vivo evidence suggests that pathological tau forms can seed further aggregation of the protein. However, many of the subsequent functional consequences following the formation of pathogenic tau aggregates are not yet fully understood. Here, we utilized the tau seeding phenomenon to induce the formation of pathogenic tau and identify intracellular consequences in a neuron culture model of AD-associated tauopathy. Primary neurons from human tau knock-in (MAPT-KI) mice were seeded with human AD brain-derived insoluble tau (AD-tau). Microscopy and biochemical assays were used to characterize the pathological tau species formed, as well as the extent of neuronal, axonal and synaptic degeneration in seeded MAPT-KI neurons. In addition, high-density microelectrode arrays were used to assess synaptic functionality in seeded MAPT-KI neuron cultures. Human-derived AD-tau seeded intracellular endogenous tau inclusions that contained AD-associated modifications (i.e. phosphorylation at the PHF1, AT8, and pS422 antibody epitopes) and adopted multiple pathogenic conformations (i.e. oligomers and exposure of an N-terminal phosphatase activating domain; PAD). Tau inclusions, containing pS422 + and PAD-exposed tau, colocalized with active glycogen synthase kinase 3β (the kinase involved in PAD-mediated axonal transport impairment) and accumulations of axonal transport cargo proteins (i.e. synaptophysin and amyloid precursor protein) in dystrophic axons. While there was no overt axonal degeneration or cell loss, intact excitatory synapses were reduced in the AD-tau neurons. Neuron cultures treated with AD-tau exhibited an N-methyl-D-aspartate receptor-dependent increase in network burst frequency when activated with glutamate as measured through high-density microelectrode arrays. Together, the data demonstrate that the AD-tau seeded MAPT-KI neuron model exhibits features associated with neuronal dysfunction resembling those that occur early in human disease (i.e. axonal pathology and dystrophy, hyperexcitability and hypersynchrony), without causing overt neurodegeneration.

Correction: How does social support influence autonomous physical learning in adolescents? Evidence from a chain mediation and latent profile analysis

Enhancing PI control in microgrids using machine-learning techniques

Abstract The integration of renewable energy sources (RES) into power systems requires sophisticated control strategies to ensure stable operation. This study presents a comprehensive framework that combines Machine Learning (ML) techniques—specifically Artificial Neural Networks (ANNs) and Reinforcement Learning (RL)—with traditional Proportional-Integral (PI) controllers to enhance microgrid control performance. Traditional PI controllers, while essential for microgrid operation with RES technologies such as solar and wind systems, face challenges in parameter tuning. Suboptimal selection of proportional gain $$\left({K}_{p}\right)$$ and integral gain $$\left({K}_{i}\right)$$ values can result in system instability or degraded performance. Our proposed ML-enhanced framework dynamically adjusts $${K}_{p}$$ based on real-time operational data and historical performance metrics, addressing these limitations. We evaluate three control strategies—traditional PI, ANN-based PI, and RL-based PI controllers—through extensive simulations of a microgrid with distributed energy resources (DERs). The RL-based controller demonstrates superior performance by reducing voltage Total Harmonic Distortion (THD) to 0.43%, compared to 16.99% for traditional PI control. The ANN-based controller achieves a THD of 0.58%, representing a 96.6% improvement over conventional methods. Both ML-enhanced approaches exceed IEEE 1547 requirements while improving settling time by 75% and frequency stability by 93%. These results validate the effectiveness of ML and deep learning techniques in enhancing microgrid stability and reliability, providing practical solutions for advanced RES management in modern power systems.

A qualitative study to understand public views on the relative value of health gains for children and young people in Australia compared to adults

Objectives Standard economic evaluation methods assume that quality-adjusted life years (QALYs) have equal social value, regardless of recipient. However, evidence suggests that people place greater social value on health gains for children. This study examines the factors driving age-related preferences for health gains. Methods Think-aloud, semi-structured interviews were conducted with Australian adolescents (n = 7), non-parents (n = 11), parents with healthy children (n = 8) and parents of children with health conditions (n = 15) over a period of four months (27 th March 2023–20 th July 2023). Participants completed Person Trade-Off (PTO) and attitudinal questions about resource allocation for improvements in life extension, mental health, mobility, and pain/discomfort choosing between interventions for adults (ages 40 or 55) and younger people (ages one month to 24). Thematic analysis was employed to identify fundamental reasoning patterns. Results Nine themes emerged, illustrating participants’ complex reasoning. They considered differences in the impact of health problems at various ages, with difficulty envisaging mental health impacts for very young children. Emotional responses were strongest around children in pain. Adolescents tended to prioritize younger people, while parents often emphasized adults’ caregiving role. Most participants prioritized based on age in PTO questions, though some adults objected to prioritizing healthcare based on age. Conclusion Choices were shaped by perceptions of the impact of the health states. These qualitative insights help to inform the development of different approaches in healthcare resource allocation highlighting the importance of involving a diverse range of participants with varying views in the decision-making process. The findings also provide insight into interpreting quantitative results from PTO tasks.

Induction of salinity tolerance in maize (Zea Mays L.) by exogenous application of ascorbic acid and gibberellic acid

Conditional VAE for personalized neurofeedback in cognitive training

Machine learning (ML) offers great potential in healthcare, especially in the analysis of complex physiological signals like electroencephalography (EEG). EEG recordings hold valuable insights into neurological function and can aid in diagnosing various conditions. In this work, we explore the use of a Conditional Variational Autoencoder (CVAE) that injects a binary health label (healthy or orthopedic impairment) into both the encoder input and the latent space coupled with the extracted features, leveraging the conditional input vector to learn representations specific to different health conditions. Our study involved using two public OpenNeuro datasets [1,2]. From the healthy dataset we randomly selected seven subjects to match the seven impaired participants; in both sets we retained the same 11 scalp channels (C3, Cz, C4, FC3, FCz, FC4, CP3, CPz, CP4, F3, F4). Six descriptors-Short time Fourier Transform (STFT), Hurst Exponent (HE), Detrended Fluctuation Analysis (DFA), Correlation Dimension (CD), Kolmogorov-Sinai Entropy (permutation entropy; KS-proxy), and the Largest Lyapunov Exponent (LLE)-are extracted channel-wise and concatenated to form the input feature vector, which distills distinct characteristics from the EEG signals. We rigorously evaluated the performance of our CVAE model in combination with each feature extraction technique. The conditional supply of class labels to both encoder and decoder enabled the CVAE to achieve 93% accuracy on the unseen test split of the dataset with precision of 93%, a recall of 93%, and an F1-score of 0.93 outperforming re-trained CNN baselines. These results highlight the promise of CVAEs and the significance of well-suited feature extraction for robust EEG classification. This work could contribute to the development of automated healthcare diagnostic tools.