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PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗

PubMed2025

Enhanced fracture detection on radiographs with AI assistance for clinicians: a systematic review and meta-analysis.

BACKGROUND: Emergency radiographic interpretation for fractures is prone to missed or misdiagnoses. Artificial intelligence (AI) is expected to become a powerful tool to assist clinicians in fracture detection. PURPOSE: A systematic review and meta-analysis was performed to assess whether AI improves clinicians' ability to detect fractures on radiographs. MATERIALS AND METHODS: A literature search was conducted in PubMed, Web of Science, and Cochrane Library for studies published between January 1, 2010, and October 10, 2025. A meta-analysis of diagnostic accuracy studies was performed using a Summary Receiver Operating Characteristic (SROC) curve. The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Subgroup analysis and meta-regression were conducted to explore potential sources of heterogeneity. RESULTS: A total of 26 studies were included . The pooled sensitivity of clinicians increased from 77% (95% CI: 72-81) to 87% (95% CI: 83-90) with AI assistance, while the pooled specificity improved from 88% (95% CI: 85-90) to 92% (95% CI: 89-94). The corresponding AUC values were 0.90 (95% CI: 0.87-0.92) before and 0.95 (95% CI: 0.93-0.97) after AI assistance. Eight studies were rated as high risk of bias. Subgroup analysis and meta-regression identified potential sources of heterogeneity, including fracture location, AI model type, high risk of bias, and reference standards. CONCLUSION: AI assistance significantly improves clinicians' diagnostic performance in detecting fractures on radiographs for extremity and trunk fractures.

Humans↗

PubMed2026

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (Rp2=0.9609, RMSE = 0.0377 mg/kg, RPD = 5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Plant Leaves↗

PubMed2026

Advanced/Novel Stenting for Pediatric Dynamic Airway Collapse.

Pediatric dynamic airway collapse is a complex condition that can impact all levels of the pediatric airway. These conditions can pose life threatening risk to pediatric patients and carry lasting impacts. While traditionally, tracheostomy has been used to address all levels of dynamic collapse, recent advances have allowed for more individualized, anatomy-specific stenting and splinting strategies for treatment. This article covers pathophysiology and the latest evidence on strategies to address nasopharyngeal, oropharyngeal, proximal trachea, and tracheobronchial dynamic collapse.

Humans↗

PubMed2026

Testing How Mindfulness Skills Change for Novice Meditators Using Headspace: Examining Trait Mindfulness and Perceived Stress as Moderators.

Mindfulness-based interventions are found to effectively reduce stress and improve mental health outcomes. Yet, it is not always clear how the mindfulness skills of attention and acceptance develop throughout the intervention. This knowledge gap is especially pertinent for novice meditators learning these skills for the first time, including whether some individuals are more prone to learning them. Using a randomized waitlist-controlled trial, we tested the effect of the app Headspace on changes in attention and acceptance over 8 weeks among participants new to mindfulness meditation. Further, we tested the moderating effects of trait mindfulness and perceived stress. Non-faculty university employees were randomized to a Headspace or waitlist control condition. Trait mindfulness and perceived stress were measured at baseline. Ecological momentary assessment survey data for attention and acceptance were collected five times a day in 4-day bursts at baseline and 2, 5, and 8 weeks post-randomisation. Attention and acceptance were significantly higher at Week 8 compared to baseline for the Headspace group, but not the control group. For the Headspace group, both skills showed significant change by Week 2. Trait mindfulness moderated this effect with those who were lower in trait mindfulness displaying greater increases in attention, but not acceptance. Perceived stress also moderated this effect with those who were lower in perceived stress displaying greater increases in attention and acceptance. Our discussion draws attention to implications for matching intervention content to individual needs to ensure participants reporting different levels of characteristics benefit from mindfulness training.

Humans↗

PubMed2026

Examining early-phase symptom trajectories in interpersonal psychotherapy versus antidepressant medication for adults with depression: A dynamic time warp network analysis.

BACKGROUND: Depression is characterized by substantial symptom heterogeneity, which is often concealed when examining total severity scores. Analyzing symptom-level change can improve our understanding of treatment effects and recovery processes. This study, therefore, examined dynamic symptom networks during early-phase interpersonal psychotherapy (IPT) and selective serotonin reuptake inhibitor (SSRI) antidepressant treatment, assessing patterns of symptom change across as well as differences between treatments. METHODS: Using weekly item-level Hamilton Depression Rating Scale (HAM-D) data from a randomized clinical trial comparing IPT and SSRIs for adults with depression, this preregistered study examined symptom trajectories in the first six weeks of treatment with Dynamic Time Warping (DTW). RESULTS: Depressive symptom trajectories and DTW-based symptom networks were largely similar for IPT and SSRI. In both conditions, changes in somatic symptoms of anxiety and middle insomnia tended to precede improvements in depressed mood. CONCLUSIONS: Early symptom change may occur outside the core affective domain, underscoring the importance of monitoring symptoms broadly. Symptom-level patterns may reflect patients' stage of recovery and provide clinically relevant information beyond total severity scores. The absence of differences in improvement patterns between IPT and SSRI suggest few indications for treatment selection based on baseline symptom profiles. Future research should replicate and extend these findings to subsequent treatment phases using more frequent assessments and a broader range of interventions.

Humans↗

PubMed2026

Functional neuroimaging subtypes of obsessive-compulsive disorder: A systematic review and meta-analysis.

Obsessive-compulsive disorder (OCD) exhibits substantial clinical heterogeneity that may reflect underlying neurobiological diversity. Neuroimaging-based subtyping may advance precision psychiatry by identifying biologically distinct subgroups with differential treatment responses. This study systematically synthesized evidence from functional neuroimaging subtyping studies in OCD to identify reproducible neurobiological subtypes, characterize their clinical profiles, and establish a consensus-based classification framework. We reviewed 40 original studies employing machine learning, clustering, normative modeling, or classification approaches, encompassing approximately 8,150 patients. Consensus clustering identified three reproducible neurobiological subtypes. The Limbic-Hyperactive subtype, comprising approximately 40% of patients, exhibited amygdala and insula hyperconnectivity, elevated anxiety levels, predominant contamination and washing symptoms, and favorable response to cognitive-behavioral therapy. The Fronto-Striatal-Hypoconnected subtype, comprising approximately 35% of patients, demonstrated reduced orbitofrontal-striatal connectivity, cognitive inflexibility, predominant checking and ordering symptoms, and a favorable response to selective serotonin reuptake inhibitors. The Global-Disrupted subtype, comprising approximately 25% of patients, exhibited widespread connectivity disruption, greater symptom severity, and poor treatment response. Support vector machine classification achieved 81.5% accuracy for subtype assignment, though classification of OCD versus healthy controls showed limited generalizability in multisite settings (AUC 0.567-0.673). These findings support a neuroimaging-based framework for personalized treatment selection but require prospective validation.

Humans↗

PubMed2026

A Dynamic Nomogram to Predict Metabolic Dysfunction-Associated Fatty Liver Disease in Patients with Metabolic Syndrome.

BACKGROUND: Metabolic syndrome (MetS) involves multiple metabolic disorders. This study aimed to identify high-risk populations for metabolic dysfunction-associated fatty liver disease (MAFLD) in patients with MetS and to establish a dynamic predictive nomogram. METHODS: A total of 627 patients with MetS from six regions in Zhejiang Province were enrolled and categorized into MAFLD and non-MAFLD groups, then randomly assigned to training and validation sets at a ratio of 7:3. Independent predictors of MAFLD were identified using least absolute shrinkage and selection operator regression and multivariable logistic regression analyses. These predictors were then used to construct a dynamic nomogram. RESULTS: A total of 627 patients with MetS were included in the final analysis, of whom 77.0% (483/627) were diagnosed with MAFLD. Multivariable logistic regression analysis identified body mass index (BMI), waist circumference (WC), total cholesterol (TC), alanine aminotransferase (ALT), MetS-defined dysglycemia, and education level as independent risk factors for MAFLD. MetS-defined dysglycemia showed the highest odds ratio (OR) for MAFLD development [OR = 1.87, 95% confidence interval (CI): 1.07-3.29]. Although the number of MetS components and the metabolic syndrome score were significantly associated with MAFLD in univariate analysis, they were not independently associated with MAFLD in the multivariate model. A dynamic nomogram for predicting MAFLD risk in patients with MetS was developed and internally validated. The area under the receiver operating characteristic curve was 0.834 (95% CI: 0.787-0.880) in the training set and 0.839 (95% CI: 0.771-0.899) in the validation set, indicating strong predictive performance. Bootstrap internal validation demonstrated good agreement between predicted and observed outcomes in calibration curves. Decision curve analysis further indicated favorable clinical applicability of the nomogram. CONCLUSION: BMI, WC, TC, ALT, MetS-defined dysglycemia, and education level are independent risk factors for MAFLD. A dynamic nomogram for predicting MAFLD risk in patients with MetS was successfully developed and validated.

Humans↗

PubMed2026

Integrative analysis of transcriptome and DNA methylome dynamics during caudal fin regeneration in silver pomfret (Pampus argenteus).

Caudal fin regeneration in teleost fish is a complex, multi-stage process involving coordinated molecular and cellular changes. While the role of epigenetic regulation particularly DNA methylation has been studied in model freshwater species such as zebrafish, its contribution to regeneration in marine teleosts remains largely unexplored. In this study, we integrated transcriptomic and DNA methylomic data to characterize the temporal dynamics of gene expression and methylation during caudal fin regeneration in the silver pomfret (Pampus argenteus). Using RNA-sequencing and reduced representation bisulfite sequencing (RRBS) at three biologically critical time points 1, 3, and 7 days post-amputation (dpa), we characterized the spatiotemporal molecular landscape of caudal fin regeneration. These time points capture the key transitional phases of wound healing and inflammation (1 dpa), blastema formation and progenitor proliferation (3 dpa), and regenerative outgrowth with tissue remodeling (7 dpa), enabling robust detection of the major molecular programs underlying epimorphic regeneration. Concurrently, CG-methylome analysis identified thousands of dynamically changing differentially methylated regions (DMRs). A strong global inverse correlation was observed between promoter methylation and gene expression. Integrative analysis pinpointed key regeneration genes (fgf20a, msxb, sox9b) whose expression was associated with dynamic methylation changes in their promoters or gene bodies. We conclude that DNA methylation is a dynamic and key regulatory layer that acts in concert with transcriptional reprogramming to coordinate tissue regeneration, providing new insights into the epigenetic mechanisms underlying complex regenerative processes in teleosts.

Animals↗

PubMed2026

Dynamics of antibiotic resistance genes co-occurrence with pathogenic and non-pathogenic bacteria throughout wastewater treatment processes.

Wastewater treatment plants (WWTPs) are recognized hotspots for antibiotic resistance genes (ARGs) and pathogenic bacteria. Despite advancements in treatment technologies, the persistence of ARGs and pathogenic bacteria remains a concern. In this study, we analyzed the dynamic changes in ARGs and bacterial communities throughout the treatment processes within an anaerobic-anoxic-oxic (AAO) WWTP over one week by using HT-qPCR coupled with 16S rRNA gene amplicon sequencing. The connectedness index, based on network analysis, showed that the dynamics of ARGs and mobile genetic elements (MGEs) were more strongly associated with potentially pathogenic bacteria than with non-pathogenic bacteria, suggesting that ARG immigration and dissemination in the WWTP were likely driven by potentially pathogenic taxa. The AAO treatment significantly reduced ARGs in final effluent (EF) (∼64 %) and residual sludge (RS) (∼81 %); however, potential hosts of ARGs such as Comamonas testosteroni and Clostridioides difficile persisted with minimal changes in relative abundance and remained detectable in EF and RS. Notably, the abundance of ARGs was lower in RS than in EF, and source tracking analysis identified influent as the primary source of ARGs and potentially pathogenic taxa in EF, underscoring the greater health risks associated with effluent discharge.

Wastewater↗

PubMed2026

Integrated widely targeted metabolomics and GC-IMS reveal dynamic flavor, nutritional, functional, and metabolic profiles in macadamia kernels during processing.

Different processing stages influence the color, flavor, and antioxidant activities of macadamia kernels. However, the biochemical mechanisms that occur during processing are not well known. This study integrated widely targeted metabolomics (UPLC-MS/MS) with GC-IMS to systematically characterize non-volatile and volatile compounds in macadamia kernels across key three sample groups: fresh kernels (FMN), low-temperature-dried kernels (DMN), and roasted kernels (BMN). A total of 622 non-volatile metabolites and 52 volatile compounds were identified. Low-temperature drying promoted the accumulation of phenolic acids and flavonoids, enhancing antioxidant capacity. Roasting degraded heat-sensitive nutrients but generated flavor compounds via Maillard reaction and lipid oxidation, shifting aroma from green to nutty notes. Nutritional assessment confirmed that roasting significantly reduced antioxidant activities and bile acid binding capacity. Pearson correlation analysis verified the key metabolite-antioxidant relationships. These findings provide critical insights into metabolic dynamics during nut processing and establish a scientific basis for optimizing thermal processing strategies.

Metabolomics↗

PubMed2026

The effect of hip abductor or external rotator strength on dynamic knee valgus in healthy subjects-a systematic review with partial meta-analysis.

BACKGROUND: Weak hip abductors and external rotators (ER) have long been suggested to cause increased dynamic knee valgus. Patients are often prescribed strengthening of these muscles with the expectation that it will reduce their risk of pain and injury. The validity of this claim remains unclear. METHOD: We conducted a systematic review and partial meta-analysis assessing the association between hip strength and knee valgus in healthy subjects. An online search was conducted in May 2026. Databases included Medline, EMBASE, CINAHL and Google Scholar. INCLUSION CRITERIA: English language, asymptomatic subjects, dynamometric hip strength, single or multi-camera kinematic analysis, and statistical tests of difference or correlations between hip abductor or ER strength and dynamic valgus. Data were extracted concerning study design, subject characteristics, relevant outcome measures and statistics. RESULTS: 22 papers qualified for inclusion. Hip abductor correlations: 12 papers found no significant correlation, 2 supported the hypothesis and 2 found evidence contrary. Hip ER correlations: 8 papers found no significant correlation, 2 supported the hypothesis and 2 were contrary. Group differences for hip abduction strength: 3 papers found no difference, 2 found significant differences in support of the hypothesis, 1 found significant difference to contrary. Group differences for ER strength: 1 paper found no significant difference. None of the partial meta-analyses achieved statistical significance. CONCLUSION: Weakness in hip abductors or external rotators may not result in increased dynamic knee valgus in healthy subjects. While continued research may further illuminate our understanding, we discuss alternative explanations.

Humans↗

PubMed2026

Peptide molecular lock-engineered nanobodies enable an oriented dual-modal immunoassay for reliable detection of Cronobacter sakazakii.

Conventional nanobody ELISAs for trace Cronobacter sakazakii in powdered infant formula suffer from random orientation and low signal output. We developed an oriented dual-modal immunoassay that combines site-specific biotinylation via a C-terminal AviTag and a peptide molecular lock, enabling controlled surface orientation while preserving nanobody structural integrity. This strategy was further integrated with phage-displayed nanobodies for multivalent amplification and both fluorescent and colorimetric readouts. The assay exhibited a broad linear range of 103-106 CFU/mL, with limits of detection (LODs) of 6.70 × 102 CFU/mL for fluorescence and 1.55 × 103 CFU/mL for colorimetry, showing improved sensitivity compared with the conventional passive adsorption-based Nb-ELISA evaluated in this study. XGBoost-based multimodal fusion improved quantitative accuracy, and SHAP analysis elucidated modality contributions. In spiked powdered infant formula samples, recoveries ranged from 92.1% to 118% with coefficients of variation below 5.98%, confirming acceptable matrix tolerance and analytical reliability.

Cronobacter sakazakii↗

PubMed2026

Single-slice Functional Lung MRI During Metronome-Paced Tachypnea Detects Changes in Regional Ventilation Dynamics After a Single Dose of Dual Bronchodilator Treatment in COPD.

Dual long-acting bronchodilators are a standard treatment in chronic obstructive pulmonary disease (COPD), aimed at alleviating dyspnea, improving exercise tolerance, and preventing exacerbations. Metronome-paced tachypnea (MPT) offers a feasible alternative to exercise testing for the evaluation of dynamic hyperinflation (DH) in COPD. Because MPT can be performed during MRI, its combination with single-slice phase-resolved functional lung (PREFUL) MRI provides a promising approach to investigate changes in regional ventilation dynamics induced by DH. This approach was evaluated in a randomized, investigator-blinded, placebo-controlled, single-dose (SD) crossover trial with a two-week extension of once daily dual bronchodilator medication, in which patients with stable COPD underwent PREFUL MRI during resting tidal breathing (RTB) and during MPT. During RTB, no significant improvements in MRI-derived parameters were observed after SD treatment compared with placebo. During MPT, however, regional ventilation, flow-volume loop correlation, its defect percentage, and end-expiratory lung area improved significantly after SD treatment compared to the placebo scan (all p < 0.02). After multi-dose treatment, five out of six measured parameters improved during MPT, when compared to the baseline scan without bronchodilator treatment (all p < 0.03). In contrast to RTB, PREFUL MRI during MPT was able to detect changes in COPD patients already after SD treatment. The combination of MPT and PREFUL MRI represents a promising method to evaluate the effects of dual bronchodilators on regional ventilation dynamics and hyperinflation.

Humans↗

PubMed2026

Effects of Dynamic Neck Sensorimotor Biofeedback Training in Individuals With Mechanical Neck Pain: A Pilot Randomized Controlled Trial.

Mechanical neck pain (MNP) is commonly accompanied by pain-related functional limitations, sensorimotor disturbances, and fear of movement, which together may contribute to persistent disability. This preliminary randomized controlled trial study investigated the short-term effects of dynamic neck sensorimotor-based biofeedback training in individuals with MNP. 20 MNP patients from outpatient clinics were assigned to a biofeedback training group or a control group. The training group underwent dynamic biofeedback exercises twice weekly for 2 weeks, whereas the control group performed repeated cervical movements without biofeedback. Outcomes included cervical kinematics as repositioning errors (RPE), movement units (MU), maximal range of motion (ROM), and subjective measures, including pain intensity, Neck Disability Index (NDI), and Fear-Avoidance Beliefs Questionnaire (FABQ). All participants completed post-intervention assessments; adherence in the training group was 100%, with no missing data and no adverse events reported. Within the biofeedback training group, participants receiving biofeedback training demonstrated greater improvements in cervical repositioning accuracy during flexion (51.95%, p = 0.04) and extension (46.67%, p = 0.02), along with reductions in fear-avoidance beliefs related to physical activity and work (p < 0.05); these changes were less apparent in the active control group. Exploratory regression analyses suggested associations between improvements in repositioning accuracy and pain reduction, and between increased cervical range of motion and improvements in fear-avoidance beliefs related to physical activity. These pilot findings suggest that dynamic sensorimotor biofeedback training may improve proprioceptive acuity and fear-avoidance beliefs in individuals with MNP, supporting further evaluation in an adequately powered randomized trial.

Humans↗

PubMed2026

Inhibitory mechanism of phloretin on the AgrA LytTR domain-agr operon complex formation and its application in beef.

Staphylococcus aureus (S. aureus) represents a major foodborne pathogen whose enterotoxin production poses significant challenges to food safety due to its high environmental resistance and limited efficacy of conventional sterilization. Since the expression of enterotoxins is predominantly governed by the agr quorum sensing system, targeting this regulatory pathway has become a strategic choice for virulence control. This study elucidated the mechanism by which phloretin, a potential quorum sensing inhibitor, interferes with the agr system to attenuate virulence. To achieve this, the recombinant AgrA LytTR domain was expressed and purified, and its interaction with phloretin was characterized using thermal shift assays (TSA), electrophoretic mobility shift assays (EMSA), and molecular dynamics (MD) simulations. The results showed that phloretin specifically binds to the AgrA LytTR domain, enhancing its thermal stability and disrupting AgrA LytTR-agr operon binding by reducing the free energy of interaction between them, without causing significant structural rearrangement. Mechanistic analysis indicated that phloretin sterically hinders key β-sheet turn residues (HIS169, ASN201, ARG233), thereby impairing DNA recognition, downregulating RNAIII transcription, and inhibiting agr signaling. In cooked beef, phloretin significantly inhibited the secretion of enterotoxins and α-hemolysin, while delaying lipid oxidation and protein degradation, and maintaining the meat texture. These findings suggested that phloretin is a multifunctional substance with anti-virulence, antioxidant, and preservative properties, demonstrating its potential as a natural food preservative.

Phloretin↗

PubMed2026

Nature-based meaning-focused photography intervention enhances subjective well-being: A three-arm randomized controlled study.

Gaining meaning from nature contact can promote subjective well-being. However, few studies have validated the effectiveness of nature-based meaning interventions in enhancing subjective well-being. This study consisted of a 7-day online intervention to examine the effects of nature-based meaning-focused photography on well-being by comparing a photo-only group, a photo + writing group, and a waiting list control group and how meaning in life mediates the relationship between nature contact and well-being. A pre-registered three-arm randomized controlled trial (groups: photo + writing group vs. photo-only group vs. control group) * (time: pre-test vs. post-test vs. 1-month follow-up) was conducted with 219 college students. In the photo + writing group, participants captured nature scenes and wrote 100-word reflections. The photo-only group only took nature photos. The primary outcomes were meaning in life and well-being, and the secondary outcome was life satisfaction. A conservative Bayesian causal forest analysis based on machine learning was used to detect both treatment and heterogeneous intervention effects. Compared with the control group, the photo + writing group showed positive effects on meaning in life, subjective well-being, and life satisfaction, with average treatment effects of 0.36, 0.27, and 0.66 standard deviations (SD), respectively. The photo-only group also showed generally positive effects on these outcomes, with average treatment effects of 0.27, 0.24, and 0.54 SD, respectively. However, these effects were not sustained after 1 month. The intervention was especially beneficial for participants from lower subjective socioeconomic status, with limited prior nature exposure, or lower baseline psychological well-being. Importantly, enhanced meaning in life helped explain how the intervention improved well-being and life satisfaction. This study also demonstrated that combining nature-based photography and reflective writing can improve well-being.

Humans↗

PubMed2025

Application of causal discovery of factors driving dissolved oxygen in estuarine environments.

Dissolved oxygen (DO) concentrations in estuarine bottom waters are a manifestation of multiple, interacting physical and biogeochemical processes, yet identifying their independent contributions remains challenging. Here, we analyze monthly water quality monitoring data from eight stations across Long Island Sound from 1994 to 2022 using a causal discovery framework (PCMCI+) and transformation of forcing variables. Our goal is to identify and isolate variables that causally influence bottom DO and improve predictive models by minimizing overfitting and multicollinearity. PCMCI+ reveals surface-layer temperature as the most important and consistent negative driver of bottom DO, followed by stratification. Wind events exhibit only brief relief by advection and mixing, while river discharge shows no direct causal link to DO, making it less influential than previously thought. Biogeochemical variables, including chlorophyll-a (Chl-a), nitrate and nitrite, and particulate carbon, influence DO through both contemporaneous and time-lagged pathways, often with signs that shift depending on the process. The derived models were evaluated by comparing skill scores, mean squared error, and Akaike Information Criterion. Both model types perform well, with coefficient of determination values exceeding 0.90 at multiple stations using only 3-5 predictors. Our analysis reveals that the best causal predictors are surface-layer temperature, stratification, Chl-a, and particle carbon. This approach provides a scalable framework for improving prediction models and understanding the mechanistic links that control the seasonal variability of DO in estuarine systems.

Estuaries↗