Browse Articles

Neural correlates of semantic typicality during episodic memory retrieval in autism spectrum disorder

Abstract This study examined the effects of item typicality (typical vs. atypical), encoding type (categorical vs. perceptual), and neurodivergence (autistic vs. neurotypical male adults) on memory discrimination and associated neuronal patterns. Despite similar overall memory discrimination performance between groups, analyses of event-related potentials revealed that neurotypicals displayed an early ERP effect, suggesting reliance on familiarity-driven processes. In contrast, autistic participants showed a later ERP modulation, indicating a reliance on recollection-based processes. Notably, relying on either familiarity or recollection influenced the activation in the post-old/new-response period, in which only neurotypical adults needed to reinstate item details for the subsequent Remember-Know-Guess (R-K-G) judgments. These findings suggest that autistic adults may recruit different cognitive processes to achieve memory performance comparable to neurotypical adults. Additionally, our results suggest that item typicality interacts with encoding type in modulating the cognitive processes underlying memory retrieval and their neural correlates in both autistic and neurotypical adults. The study highlights the need to investigate the role of semantic processes in episodic memory retrieval in both neurotypical and autistic individuals.

In vitro effects of potato glycoalkaloids on plant-pathogens, beneficial microbes, and Arabidopsis thaliana

Abstract Potato glycoalkaloids (PGAs), α-solanine and α-chaconine, are secondary metabolites related to plant defense. Highly concentrated in the upper part of potato plants, they exhibit antimicrobial properties. Seeking more sustainable crop protection strategies, this study investigates the effects of PGAs on plant pathogens and beneficial organisms. These organisms were exposed to different PGA concentrations (0.98 to 250 ppm), with evaluations focusing on developmental and survival metrics. Key findings highlight α-chaconine as the more potent compound, causing significantly stronger adverse effects across tested organisms. E.g., α-chaconine (≤ 25 ppm) reduced nematode mobility by 43%, host attraction by > 45%, and infection rates by 63%. At 250 ppm, α-solanine moderately reduced mycelial growth, while α-chaconine reduced it by 78%. Crucially, beneficial organisms experienced minimal growth impairment (≤ 19%) even at the highest concentration (250 ppm). Arabidopsis thaliana seedling development was impaired by both PGAs, and seedlings exposed to these compounds exhibited a strong, transient oxidative burst, indicating direct stress activation. Pretreatment with PGAs did not induce priming effects but even decreased subsequent elicitor-induced responses. The selective toxicity against pathogens, coupled with minimal impact on beneficial species, positions PGAs, particularly α-chaconine, as promising starting points for sustainable crop protection strategies.

CH–π Interactions Elucidated at the Single-Molecule Level

Development of a novel machine learning-based adaptive resampling algorithm for nuclear data processing

Abstract Efficient processing of nuclear cross-sections data is critical for advanced reactor physics and safety assessments. Existing workflows of using nuclear data in Hierarchical Data Format, version 5 (HDF5 format) rely on intermediate file formats, such as A Compact ENDF (ACE) files generated via NJOY, which introduce inefficiencies in nuclear data processing. This work presents two novel computational techniques that streamline nuclear data processing and modification. First, a machine learning-based resampling algorithm is presented for nuclear cross-section data stored in HDF5 format, designed to intelligently retain critical threshold points while reducing data redundancy. Second, a direct HDF5 modification framework is introduced, eliminating the need for legacy file conversion steps and enabling direct edits to OpenMC-compatible nuclear data libraries. This methodology employs an adaptive resampling strategy that dynamically adjusts point densities across diverse neutron energy regions, preserving resonance structures and threshold behaviors while achieving significant data compression. Benchmarking against established models−such as K-Nearest Neighbors and Gaussian Processes−indicates that the ML-based approach offers lower errors and enhanced computational efficiency. This integrated framework improves nuclear data accessibility and expedites simulations, reactor core design, uncertainty quantification, and neutronics analysis.

Comprehensive structural model for the evaluation of HME humidification properties

Abstract HMEs (heat and moisture exchangers) are employed in clinical practice to humidify the inspired air in intubated patients. We aimed to develop and experimentally validate a mass based model describing the water input-output equilibrium in the presence of HMEs. The model should serve as a universally applicable basis for the evaluation of the fundamental HME humidification properties performance (mg reversibly stored per breath) and efficiency. A plug flow model was designed using three different locations (gas inlet, moisture benefit and water output) for inspiratory-expiratory flow and humidity measurements. We developed a complete set of algorithms for the calculation of all relevant metrics. Measurements also assessed humidity dependent volume changes. We proved the concept through the validation of humidity predictions without HME in the test setup at target humidities of 37 and 44 mg/l respectively with the inspiratory volume of 0.25, 0.50 and 0.75 l. The concept was applied in the evaluation of a random sample of HMEs. Differences between prediction and measurements ranged between − 1.89 and 2.24 mg/l. For moisture benefit a mean difference of 0.15 mg/l (sd 0.69) was found and for water output of -0.48 mg/l (sd 0.98). Humidification caused a significant volume increase up to 6.6% with respect to the inspired dry air. HME performance results of five HME brands ranged from 5 to 30 mg overall. Performance results derived from moisture benefit and water output agreed within 5% of the common mean. Efficiency results can be above 80% over the whole measurement range or below 20% at high water load depending on the HME brand. Thus, HMEs can be sorted by registering efficiency at the upper bound of the tidal volume range. HMEs for laryngectomized patients (LE-HMEs) showed much lower performances between 2 and 5 mg per breath due to their small size. A novel comprehensive research environment, both theoretical and experimental, enables the assessment of HME properties and gives qualitative and quantitative definitions of HME performance and efficiency for clinical use.

Retraction Note: TFPI-2 suppresses breast cancer cell proliferation and invasion through regulation of ERK signaling and interaction with actinin-4 and myosin-9

AI-assisted phenotyping in a zebrafish hypophosphatasia model enables early and precise detection of skeletal alterations

Abstract Hypophosphatasia (HPP) is a rare genetic disorder mainly affecting bone and tooth mineralization in patients due to ALPL gene mutations. Understanding genotype-phenotype correlations in HPP remains challenging due to different severities and the disease’s heterogeneity. To address this, we established a novel zebrafish animal model (alpl wue7), which mimics severe HPP disease forms. To bypass limitations in human-based phenotypic classification of skeletal alterations in this transgenic line, we developed and trained an artificial intelligence (AI) model capable of image-based classification with 68% accuracy—an improvement of 79% over manual classification. Our AI model could successfully identify early developmental alterations independent of altered image magnification, coloration quality and executing scientists. Using attention rollout, we further visualized AI decision-making, revealing not only expected focus on early bone structures but also unexpected emphasis on the otoliths—parts of the zebrafish’s hearing and balancing organ. We see applications of our AI system in analyzing other skeletal disorder models as well as in providing an unbiased, high-throughput phenotypic rescue quantification assay for potential drug screening applications in zebrafish larvae. Overall, our findings establish an integrated platform for studying HPP and open new avenues for AI-assisted phenotyping and therapeutic discovery.

A multiscale computational investigation for protection of carbon steel surface by pyrazolo-pyrimidine derivatives

Abstract The potential corrosion inhibition properties of pyrazolo-pyrimidine derivatives with extended π system for carbon steel in acidic medium were investigated through a combined DFT, Monte Carlo and molecular dynamic simulations to establish the relationship between electronic properties and adsorption behavior. Geometrical optimization, quantum chemical parameters, (FT-IR) and UV–Vis spectra were explored. The charge transfer and primary sites of adsorption corresponding to enhancement the corrosion inhibition were identified by NBO and Fukui analysis. The Pauli exchange repulsion effect was utilized for the examination of Localized Orbital Locator and Electron Localization Function. To identify bonding and anti-bonding interactions, a study of non-covalent interaction was conducted. Monte Carlo and molecular dynamic simulations predicted strong adsorption energies and explained the stability of inhibitor metal complexes. The ethyl ester substituted inhibitor showed the smallest energy gap (4.640 eV) and the highest negative adsorption energy (− 129.998 kcal·mol−1), indicating superior adsorption capability and inhibition efficiency. These findings offer theoretical framework for designing efficient corrosion inhibitors with extended π systems for carbon steel protection.

MOFClassifier: A Machine Learning Approach for Validating Computation-Ready Metal–Organic Frameworks

Thermo-mechanical behavior and spalling resistance of alkali-activated slag versus cement mortars under rapid high-temperature exposure

Abstract The susceptibility of high-strength cementitious composites to explosive spalling under elevated temperatures necessitates the development of sustainable, fire-resistant alternatives for structural applications. This study comparatively evaluates the thermo-mechanical performance and spalling resistance of high-strength alkali-activated slag mortar (HSAAM) and ordinary Portland cement-based mortar (HSCM) under rapid fire scenarios. HSAAM was synthesized using granulated blast-furnace slag (GGBFS), while HSCM incorporated silica fume (SF) to achieve comparable compressive strength. Specimens were exposed to short-term elevated temperatures (200–600 °C) at 10 °C/min, with dwell times of 10–30 min, followed by furnace cooling or water quenching. Residual mechanical properties (compressive strength, tensile strength, impact resistance), thermal insulation, mass loss, and microstructural evolution were systematically analyzed. Results revealed that HSAAM exhibited complete spalling resistance up to 600 °C, whereas HSCM suffered partial spalling at 400 °C and catastrophic failure under water cooling. After 10 min at 400 °C, HSAAM retained 66.8% compressive strength (52 MPa) and 82% tensile strength (1.66 MPa), while HSCM retained 102% compressive strength (77.5 MPa) but experienced 10% specimen failure. HSAAM demonstrated superior thermal insulation, with core temperatures 44% lower than HSCM at 400 °C. Microstructural analysis via SEM/EDS identified a nano-porous matrix in HSAAM, facilitating vapor release and mitigating internal pressure. These findings position alkali-activated slag mortars as a robust, fire-resilient alternative to conventional cementitious systems in high-temperature environments.

Author Correction: Selective manipulation of the inositol metabolic pathway for induction of salt-tolerance in indica rice variety

Acute effects of a high fat and high carbohydrate meal with and without subsequent physical activity on cardiac autonomic modulation in people with type 2 diabetes

Abstract The impact of different dietary interventions on autonomic cardiac modulation (ACM) in people with type 2 diabetes (T2D) is a remarkable, albeit scarcely studied topic. The aim of this secondary outcome analysis was to evaluate the effects of a high-carbohydrate (CHO) and high-fat meal (FAT) with and without subsequent physical activity (PA, CHO + PA and FAT + PA) on baseline electrocardiographic and heart rate variability (HRV) parameters in people with T2D. Eleven individuals with T2D (five females, 65.8 ± 5.9 years, body mass index: 29.5 ± 5.2 kg·m−2, HbA1c: 7.0 ± 0.8%) participated in this study. Electrocardiogram (ECG) and HRV parameters before and after the meal intake as well as during physical activity, a 30-minute self-paced treadmill walk, were analyzed. Baseline ECG parameters included resting heart rate (73 ± 8 beats per minute), PQ interval (160 ± 24 ms), QRS interval (92 ± 11 ms), QT (387 ± 17 ms) and QTc time interval (420 ± 20 ms) without clinically relevant dynamics. No negative impact of either dietary interventions or PA on HRV parameters, i.e. standard deviation of the NN interval (SDNN, (F [1.9] = 0.28), root mean square of successive differences (RMSSD, (F [1.9] = 0.09) and ln LF/HF (low frequency/high frequency) (F [1.9] = 0.00) could be revealed displaying preserved ACM. In conclusion, our secondary outcome analysis displayed preserved ACM in T2D independently of dietary intervention or additional PA. Finally, our research might strengthen the scientific data on dietary intervention in T2D and demonstrate future preventive options in the context of T2D and nutrition.

Chemical Bonding between Helium and Fluorine under Pressure

Manganese-Catalyzed Asymmetric Transfer Hydrogenation of Heteroatom-Containing Imines and Diarylimines

Post-vaccination SARS-CoV-2 infections among healthcare workers in a tertiary hospital in Ghana

Introduction Vaccines remain the most effective preventive measure against the ever-changing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. However, vaccine access remains unequal, leaving healthcare workers in low- and middle-income countries (LMICs) like Ghana at increased risk, despite early prioritisation. These inequities threaten both individual safety and the resilience of health systems. Moreover, SARS-CoV-2 infections continue to occur, particularly with emerging variants, compounding these risks. This study aimed to investigate the incidence and risk factors associated with post-vaccination SARS-CoV-2 infections among healthcare workers at a tertiary hospital in Ghana following the administration of the ChAdOx1nCoV-19 vaccine. Method We conducted a prospective cohort study of 4252 healthcare workers aged 18 and above, who tested negative for the SARS-CoV-2, and partially or fully vaccinated with the ChAdOx1nCoV-19 vaccine at baseline. After completing the baseline questionnaire, healthcare workers were followed up for one year. Results 2283 out of the 4252 (53.7%) healthcare workers had post-vaccination infections, with an overall incidence of 95.7 cases per 100 person-years (95% CI: 91.8–99.7) of follow-up. The incidence of breakthrough infection was 82.0 cases per 100 person-years (95% Cl 78.0–86.0). In a multivariable Cox regression, age, vaccination status, occupation, clinical stations, frontline status and previous SARS-CoV-2 infections were significantly associated with post-vaccination infections. Compared to non-clinical healthcare workers, nurses (HR = 1.91, 95% CI: 1.69–2.17) and doctors (HR = 1.37, 95% CI: 1.24–1.73) had a higher risk of post-vaccination infections. Similarly, elderly individuals (HR = 1.04, 95% CI: 1.02–1.05) and those with comorbidities (HR = 1.86, 95% CI: 1.67–1.73) were more likely to develop post-vaccination infections. Frontline healthcare workers and healthcare workers stationed at the point-of-entry services (emergency and outpatient clinics) had a high rate of infections. However, previous SARS-CoV-2 infections (HR = 0.80, 95% CI: 0.71–0.53) and full vaccination (HR = 0.56, 95% CI: 0.51–0.62) conferred some protection, despite an overall rise in infection post vaccination incidence. Conclusion In conclusion, the results of our study suggest a high incidence of post-vaccination infections among healthcare workers in the context of varying epidemic waves. Additionally, the study identified partial or incomplete vaccination, elderly workers, comorbidities, frontline workers, nurses and point-of-entry service roles as high-risk factors for post-vaccination infections. These findings reinforce the need for tailored booster strategies and strengthened protection for high-risk healthcare workers in LMIC settings.

More than just one man and his dog: The many impacts of puppy acquisition on the mental health of families including children in the UK

Many puppies were acquired during the COVID-19 pandemic to boost families’ mental health. The aim of this study was to characterise the nature, benefits and challenges of dog-child interactions as perceived by UK adult caregivers and co-habiting children aged 8–17 years. In 2023, a three-part online survey was administered incorporating qualitative and quantitative questions. We included two respondent groups: households owning (a) a puppy acquired during 2019 and (b) a puppy acquired during the COVID-19 pandemic in 2020–2021. Statistical analyses explored differences between ownership groups; free-text data was thematically analysed. Valid responses were collected from n = 382 caregivers and n = 216 children. Puppies’ primary adult caregivers were 95% female. Over one-third (37.3%) of caregivers found living with a puppy harder than expected; first-time owners were significantly more likely than experienced owners to find child-puppy interactions challenging. Almost all children were allowed to physically interact with their dog in ways previous studies have associated with an increased bite risk. Three themes were constructed from the free-text data, highlighting: (1) perceived positive aspects of dog ownership, particularly the importance to children and caregivers of close child-dog physical interactions; (2) challenges of managing a dog in a family home including negotiating responsibility for care between family members and establishing safe child-dog boundaries; (3) how one dog could differentially impact multiple household members, including that a single dog-person relationship could impact whole-household dynamics. These findings underscore the importance of involving all household members in human-dog research and highlight the unpredictability of whether acquisition motivations will align with outcomes. Resources are needed to help families safely and successfully integrate puppies into their homes, particularly in managing the evident associated maternal mental load.

Sex differences in clinical characteristics, timeliness of care, and in-hospital outcomes of adult non-trauma patients in the emergency department

Background Data on sex differences in the clinical characteristics and outcomes of unselected emergency department (ED) patients are limited. We conducted a retrospective real-world cohort study to evaluate sex differences in clinical characteristics, ED timeliness of care, and in-hospital outcomes of adult non-trauma ED patients. Methods Data from adult non-trauma patients who paid their first ED visit to a tertiary medical center from January 2018 to Jun 2020 were retrospectively analyzed. The patients were divided into male and female cohorts. The ED length of stay (LOS) was measured as the outcome of timeliness of care, whereas hospital admission, hospital LOS, and in-hospital mortality were measured as the in-hospital outcomes. Multivariate regression analyses were utilized to investigate the association between sex and outcomes. Results Of the 43,661 patients included, 49.2% were males. The male cohort was older and had higher incidences of interhospital transfer and Taiwan Triage and Acuity Scale (TTAS) levels 1 and 2, higher mean Charlson comorbidity index, and more comorbidities than the female cohort. The male cohort had longer ED LOS and hospital LOS and higher incidences of hospital admission and in-hospital mortality. Multivariate regression analyses revealed that the male sex was an independent risk factor associated with adverse outcomes after adjustment for confounding factors. All these adverse outcomes were found in the male subgroup with TTAS levels 1–3. Conclusions Our study identified sex differences in clinical characteristics, ED timeliness of care, and in-hospital outcomes of adult non-trauma ED patients. Male patients had various unfavorable conditions, including having older age, higher acuity levels, and more comorbidities, and were at higher unadjusted and adjusted risk for adverse outcomes on ED LOS, hospital admission, hospital LOS, and in-hospital mortality. The male subgroup with TTAS levels 1–3 was vulnerable to the negative impact of sex on these outcomes.

An improved artificial gorilla troops optimizer for BP neural network-based housing price prediction

In the context of global economic austerity in the post epidemic era, housing, as one of the basic human needs, has become particularly important for accurate prediction of house prices. BP neural network is widely used in prediction tasks, but their performance is easily affected by weights and biases, and thus metaheuristic algorithms are needed to optimize the network parameters. Firstly, to address the shortcomings of the artificial Gorilla Troops Optimizer (GTO) in such optimization tasks, such as reduced population diversity, easy to fall into local optimal solutions and slow convergence, this paper proposes a fitness allocation strategy, a Cauchy variation strategy, and an elite evolution mechanism to improve the algorithm, which in turn results in an improved artificial Gorilla Troops Optimizer (IGTO). Subsequently, a BP neural network house price prediction model based on IGTO is constructed and experiments are conducted on four datasets, namely, Boston, California-Bay, California-Land and Taiwan. The experiments are first compared with eleven other swarm intelligence algorithms and then with four machine learning models, and the results show that IGTO-BPNN improved 17.66%, 18.27%, 28.10%, 49.35% and 24.83% on five evaluation metrics, namely, MAE, MAPE, R2, RMSE, and SMAPE, respectively. The improvement of these indicators fully proves the superiority and effectiveness of IGTO-BPNN in house price prediction.

The GravyTrain toolbox for molecular cell biology

Yeast genetics has the power for thorough investigation of complex systems in molecular cell biology. Here, we present GravyTrain, a comprehensive repository of constructs for genomic modifications, including gene deletions or tagging with fusion tags for robust protein characterization and manipulation. The modular cloning scheme employed by GravyTrain allows shuffling of elements between constructs and potentially the application of included protein tags to research beyond yeast. The experimental potential of GravyTrain is demonstrated by the de novo construction of a library of strains for studying autophagy in yeast.

Differentiated brand building of traditional mountain scenic spots in Jiangxi Province: Insights from tourists’ perception images and sentiment characteristics

Understanding the perceptions and sentiment responses of tourists to traditional mountain scenic spots is crucial. This understanding is a cornerstone for improving brand image, optimizing space quality, and fostering sentiment connections between tourists and natural landscapes. Our study aims to address the homogenization issue in mountain scenic spots’ brand building. Using online comments and travel blogs as primary data, a mixed-methods approach integrating grounded theory, correspondence analysis, TF-IDF, sentiment analysis, and IPA analysis was employed. The result revealed several significant findings: (1) The tourist perception image represents a multidimensional dynamic system that includes resources, service, sightseeing, and time-space images. Different mountain scenic spots have contrasting characteristics regarding tourism perception images, with the sightseeing image proving a pivotal role in shaping these differences. (2) Among all mountain scenic spots, positive sentiment generally outweighs negative or neutral sentiments, while there are differences in sentiment preferences among perception images. (3) Interactions between tourists’ perception images and their sentiment are fundamental to the establishment of the brand of a scenic site. This research constructs a theoretical model for the generation path of tourism brand image, deepens the understanding of cognitive-affective interactions, improves the perception image system, and provides methodological support for big data-based tourism research. Hence, this study offers insights for the differentiated brand building and sustainable development of traditional mountain scenic spots.