Browse Articles

A steel slag-activated column for co-removal of sulfate and metals from acid mine drainage

Daily Mosnodenvir as Dengue Prophylaxis in a Controlled Human Infection Model

Identifying the factors governing internal state switches during nonstationary sensory decision-making

Abstract Traditional models of perceptual decision-making fail to capture dynamic strategy switching in non-stationary environments, and the factors governing these switches remain unknown. To address this gap, we developed an advanced internal state model with input-driven transitions and observations. Our approach employs a hidden Markov model (HMM) coupled with two sets of per-state generalized linear models (GLMs): a Bernoulli GLM for state- and stimulus-dependent choices, and a multinomial GLM for input-dependent transitions between states. We applied our model to a decision-making task in a non-stationary environment, analyzing hundreds of thousands of trials from a cohort of mice, and found that their behavior can be accurately described by a four-state model. This model identified two engaged states with low biases relative to the stimulus and two disengaged states with pronounced biases relative to the stimulus. Our analyses revealed that mice preferentially used left-bias strategies during left-bias stimulus blocks, and right-bias strategies during right-bias stimulus blocks, achieving high performance even in disengaged states by biasing choices toward the side with greater prior probability. Our model showed that past choices and past stimuli predicted transitions between left- and right-bias states, while past rewards predicted transitions between engaged and disengaged states. In particular, greater past reward predicted transition to disengaged states, suggesting that disengagement may be associated with satiety. Our approach uncovers links between animal behavior, input regressors, and state transitions, highlighting the complexity of adaptive strategies. This provides a foundation for future research in dynamic decision-making models.

Emotion induction by western music across personality types using internet of things technology

Prevalence and predictors of problematic smartphone use in a sample of Spanish undergraduate students

Discontinuous orbital angular momentum metasurface holography

Observing the impact of renewable electricity on the emission factors of electric vehicles using electricity generation data

Differences in smell and taste performance and food liking between patients with stroke and healthy controls

A prospective trial for breast cancer diagnosis by canine odorology

A mechanistic approach for intracranial pressure (ICP) level estimation

An SN2 reaction mechanism for hydrolysis of siloxane linkages and the implications for dissolution of quartz

Sulfophenylated centimeter-sized graphene membrane in a direct methanol fuel cell

Cathepsin B as a potential serum biomarker for early diagnosis and progression of diabetic foot ulcer complicated with peripheral vascular disease

Abstract The existing diagnostic methods of diabetic foot ulcer (DFU) complicated with peripheral vascular disease (PVD) lack sufficient potential for early identification, which leads to slow wound healing, amputation and even death. Thus, this study aimed to explore the potential serum biomarkers of DFU complicated with PVD. A target gene of DFU complicated with PVD was identified using single-cell transcriptome analysis. The immunohistochemistry, ELISA, clinical correlation analysis, tubulogenesis assay, CCK8 assay, and scratch assay were used to verify the correlation between this target gene and DFU complicated with PVD. The ELISA experiment was used to detect the target gene in serum. In this study, the result of PPI in single-cell transcriptomes showed that cathepsin B (CTSB) was enriched in vascular endothelial cells of DFU. The immunohistochemistry and ELISA results revealed that CTSB was highly expressed in the tissues and serum of patients with the combination of DFU and PVD, and this expression increased with the increase of the Wagner grade of DFU. Clinical correlation analysis indicated that CTSB expression is positively correlated with the clinical indicators of the combination of DFU and PVD. Knockdown of CTSB promoted tubulogenesis, proliferation and migration of vascular endothelial cells and overexpression of CTSB has the opposite effect. CTSB, a secretory protein, can be detected as a diagnostic biomarker in serum. Therefore, this study suggested that CTSB can be used as a potential serum diagnostic biomarker for DFU complicated with PVD, which is helpful for the early diagnosis of this disease, prognosis monitoring and adjustment of treatment plans.

An interactive heuristic model to test ecological and evolutionary hypotheses on incipient polyploid species

Abstract Polyploidization is associated with lineage-specific changes that promote divergence and speciation. Knowledge about the establishment of neopolyploids is fragmentary. We use an open-source multi-agent software to build a scalable easy-to-use command center for analysing complex diploid-polyploid interactions. The workspace is a multilayered environment whose eco-variables fluctuate between generations. Reproductive syndromes, recombinant/clonal inheritance and complex traits (adaptivity, niche breadth, dispersal) are used to elicit fitness values and monitor population spatial dynamics. Neopolyploidization was recurrent, polytopic and heterogeneous in time. Increasing rates of unreduced gametes accelerated the establishment of neopolyploids but removed the role of triploids. Under standard rates and heterogeneous environment, model-based evidence shows that (1) a large proportion of polyploidization events are unsuccessful, (2) parental traits and local conditions prime a loss of diploid fitness that benefited eco-geographic structured polyploid success, (3) self-fertility and apomixis improve the rate of polyploidization, and shorten the time required for demographic establishment, and (4) ecological niche shifts promote cytotype coexistence, but niche expansion favors establishment and foster cytotype displacement. The modelling framework offers opportunities for in-depth lineage-specific analyses of the spatiotemporal dynamics and evolutionary differentiation in diploid-polyploid systems.

Mutations in the β-tubulin TUBB impair ciliogenesis and are associated with ciliopathy-like phenotypes

Stability and control mechanism of nonlinear horizontal vibration for rolling system with gyroscope precession effect

Risk factors for corneal transplantation in Fuchs endothelial corneal dystrophy from a large Thai cohort

Transition towards plate tectonics tracked in the metamorphic signature of Neoarchean synmagmatic transpression

Abstract Secular mantle cooling has progressively strengthened Earth’s lithosphere, enabling a variety of tectonic styles. The emergence of transpressional orogens in the Neoarchean is interpreted to reflect this strengthening, during a transitional phase leading to plate tectonics. However, direct constraints on lithospheric strength remain limited due to the fragmentary Archean rock record and the scarcity of structurally and temporally constrained metamorphic data. Here we present metamorphic data from two major Neoarchean shear zone networks in the transpressional Yilgarn Orogen (Western Australia), showing shearing events with vertical components of displacement of ~10 km, and burial–exhumation rates comparable to those in Phanerozoic orogens. Our findings support numerical predictions of a mechanically strong Neoarchean lithosphere capable of sustaining significant orogenic thickening. This provides new constraints on the rheology of early continental lithosphere and offers insight into the geodynamic processes that preceded the full establishment of plate tectonics.

Artificial neural network (ANN) based prediction of proppant settling in horizontal wellbores during hydraulic fracturing

Abstract Proppant transport and settling in horizontal wellbores is a major challenge in hydraulic fracturing, leading to problems such as sand production, equipment wear, wellbore blockages, and reduced production rates. Traditional empirical models are often limited in accuracy because the physical relationships involved are highly nonlinear and complex. In this study, Artificial Neural Networks (ANNs) were used to develop predictive models for sand settling in horizontal wellbores during hydraulic fracturing. In this work, the data were obtained from controlled laboratory experiments that simulated horizontal wellbore sections with different perforation clusters. Key parameters such as proppant diameter, injection rate, perforation orientation, and number of perforations were analyzed. Two ANN models were developed: Model A used all nine measured parameters, while Model B used five selected parameters identified through correlation analysis. Model performance was evaluated using statistical metrics including the coefficient of determination (R²), Average Absolute Difference (AAD), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Additionally, comparative analyses with Random Forest and Gradient Boosting algorithms confirmed the superior performance of the ANN models, and an explicit neural-network-based correlation was formulated for direct engineering use. Results show that Model A achieved an R² of 0.96 and Model B achieved 0.89, demonstrating that input reduction only slightly reduced predictive accuracy. Both models successfully captured nonlinear relationships, confirming that injection rate, perforation orientation, and perforation number are the most critical factors influencing sand settling. To further test their robustness, cross-validation was carried out using independent experimental data from the literature. Model A achieved an R² of 0.82 and Model B achieved 0.75, showing that both models generalized well to independent datasets, with Model A slightly outperforming Model B. This study provides a novel machine-learning approach for predicting proppant settling in horizontal wellbores under fracturing conditions from experimental data. In contrast to empirical models, the ANN-based predictor accounts for multivariable nonlinear interactions and can be deployed for real-time decision support. The findings contribute to enhanced hydraulic fracturing designs, improved wellbore stability, and reduced operational challenges related to sand production and equipment erosion. Overall, the ANN-based models provide quick and reliable predictions of sand settling, outperforming traditional empirical approaches and offering practical tools for optimizing hydraulic fracturing designs. By accounting for nonlinear interactions and validating independent data, the models demonstrate strong potential for real-time decision support, improved wellbore stability, and reduced operational challenges related to sand production and equipment erosion.

SplitWise regression for capturing nonlinear effects in interpretable model selection

Abstract Capturing nonlinear relationships while maintaining interpretability remains a persistent challenge in regression modeling. We introduce SplitWise, a stepwise regression framework that adaptively transforms numeric predictors into threshold-based binary features using shallow decision trees—only when such transformations improve model fit according to the Akaike or Bayesian Information Criterion. This design preserves the transparency of linear models while flexibly capturing threshold-based nonlinear effects, positioning SplitWise between classical linear and interpretable nonlinear regression. SplitWise retains a single, globally linear equation that selectively incorporates data-driven thresholds—yielding models that remain straightforward to interpret and verify. Across synthetic scenarios with nonlinear signal patterns, SplitWise reduced median RMSE by 7–14% relative to the best-performing interpretable linear baseline and improved variable-selection accuracy (median Matthews Correlation Coefficient up to $$\sim$$ 0.79 vs.  $$\sim$$ 0.51 for LASSO). On real datasets, SplitWise matched or slightly improved RMSE while selecting fewer predictors. For instance, on Wine Quality (White), it improved RMSE from 0.756 to 0.752 and on Wine Quality (Red) from 0.654 to 0.649, using 6–10 predictors. On Bodyfat, it achieved 3.48–3.49 RMSE with four predictors, comparable to Elastic Net (3.41–3.48 RMSE) but with smaller models.