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Breaking Supercapacitor Symmetry Enhances Electrochemical Carbon Dioxide Capture

Protective role of Lagenaria Siceraria seed oil against furan-induced toxicity: Histopathological, biochemical, and molecular insights in male Albino rats

This study aimed to assess the protective effects of Lagenaria siceraria seed oil (LSO) on fifty male Albino rats subjected to furan exposure. Furan (FU) is a small, heterocyclic compound present in the volatile fraction of various thermally processed foods and beverages. Rats were categorized into five groups, each comprising ten rats. Group 1 served as the control group, receiving corn oil. Group 2 received LSO (3 g/kg body weight orally) for 28 days. Rats in Group 3 (FU-exposed group) received an oral administration of FU at a dosage of 16 mg/kg body weight each day for 28 days. Rats in Group 4 (Therapeutic co-treated group) were administered both LSO and subsequent FU exposure according to the previously outlined dosage regimen for 28 days. Rats in Group 5 (Protective co-treated group) received LSO seed oil for 14 days as protection then received Fu at the same mentioned doses of Fu until the end of experiment. Rats administered FU and/or LSO gained noticeably more weight than the control group. LSO significantly decreased AST and LDH levels in both the protection and treatment groups as compared to the FU-only group. It also assisted in restoring testosterone and luteinizing hormone (LH) levels that were decreased by FU, especially in the protected group. LSO also reduced kidney damage markers and normalized biomarker levels when administered with FU. The LSO-only group demonstrated normal immune response markers, similar to the control group. By changing MDA levels and increasing SOD, GSH, and TAC levels, co-treatment with LSO enhanced liver health. The control and LSO groups displayed normal spleen structure, whereas the LSO/FU group had normal seminiferous tubules with mild edema and congestion. Overall, LSO demonstrated protective and therapeutic benefits against FU-induced damage in rats.

Menstrual cycle inspired latent diffusion model for image augmentation in energy production

Abstract In the energy production domain, image classification is critical for monitoring, diagnostics, and operational optimization tasks. Latent diffusion models (LDMs) have shown potential in generating diverse images during the augmentation process based on text input. However, they are hindered by pixel integrity, texture consistency, and mode collapse. This paper introduces menstrual cycle-inspired latent diffusion model (MCI-LDM), a novel framework that addresses these challenges with three key modifications. First, a menstrual cycle-inspired metaheuristic algorithm is integrated to improve generated images’ pixel integrity and structural coherence. Second, an adaptive attention mechanism is employed to dynamically focus on critical regions during image generation, ensuring that fine details are preserved. Third, a multi-scale feature enhancement module is incorporated to capture global structures and local textures, mitigating mode collapse and enhancing overall image quality. Extensive experiments were conducted on five energy-related datasets, demonstrating the superior performance of MCI-LDM in terms of image augmentation, diversity, and generation accuracy. The results highlight the efficiency of the proposed model, making it a valuable tool for improving image classification and data augmentation in energy sector applications. MCI-LDM outperforms LDM by generating more diverse images, with a higher Inception Score (7.1 vs. 5.4) and a lower Fréchet Inception Distance (22.5 vs. 35.2), indicating better quality and variation. Additionally, MCI-LDM preserves image integrity more effectively, achieving superior PSNR (32.7 dB vs. 28.5 dB) and SSIM (0.92 vs. 0.78).

Branch-Selective Olefin Hydroaminoalkylation from Ti(III)–Al Bimetallic Intermediates Evidenced by EPR Hyperfine Spectroscopy and DFT Calculations

Characterisation of facial expressions and behaviours of horses in response to positive and negative emotional anticipation using network analysis

The welfare of an animal is closely linked to their emotional experiences, making it essential to identify reliable indicators of these emotions. This study aimed to identify behaviours and facial movements in horses experiencing contrasting emotional valence, triggered by the anticipation of a positive condition (going to pasture) or a negative condition (going alone to a novel environment). Twenty horses were daily trained to wait in a starting box before being exposed to these two conditions. After one week of positive training or negative training, we analysed horses’ behaviours, cortisol variations, and facial movements while they waited in their starting box. First, we confirmed that the two conditions induced contrasting emotional valence, as evidenced by the shorter time taken to approach in the positive condition compared to the negative, and by the higher maximal heart rate and cortisol variation in the negative condition. Then using the Equine Facial Action Coding System (EquiFACS) and network analysis (NetFACS) we revealed distinct behaviours and facial expression profiles. In positive anticipation, the horses exhibited a greater range of behaviours, including shaking their heads from side to side, stepping back, sniffing, and pawing at the ground. Additionally, two distinct facial expression profiles were identified as specific to positive and negative anticipation. In positive anticipation, the horses displayed a higher neck, accompanied by a greater frequency of half-blinks and mouth movements. Conversely, in negative anticipation, the horses exhibited a medium neck, with ears backward accompanied by more flattened ears and expressed more nostril movements. The findings highlight the importance of these indicators in characterising horses’ emotions and emphasise their significance for assessing equine welfare.

Dynamic mechanical response evaluation of woven carbon fiber reinforced rubber laminated composites under high strain rates

Microscopic Mechanical Force-Driven Amorphization of Metal–Organic Frameworks

New technology for an ancient fish: A lamprey life cycle modeling tool with an R Shiny application

Lampreys (Petromyzontiformes) are an ancient group of fishes with complex life histories. We created a life cycle model that includes an R Shiny interactive web application interface to simulate abundance by life stage. This will allow scientists and managers to connect available demographic information in a framework that can be applied to questions regarding lamprey biology and conservation. We used Pacific lamprey (Entosphenus tridentatus) as a case study to highlight the utility of this model. We applied a global sensitivity analysis to explore the importance of individual life stage parameters to overall population size, and to better understand the implications of existing gaps in knowledge. We also provided example analyses of selected management scenarios (dam passage, fish translocations, and hatchery additions) influencing Pacific lamprey in fresh water. These applications illustrate how the model can be applied to inform conservation efforts. This tool will provide new capabilities for users to explore their own questions about lamprey biology and conservation. Simulations can hone hypotheses and predictions, which can then be empirically tested in the real world.

Real-world evidence couples genomic biomarkers with therapeutic outcomes for canine hemangiosarcoma

Dynamic Kinetic Asymmetric Hydroacylation: Racemization by Soft Enolization

Origin, trophic transfer and recycling of particulate organic matter in two upwelling bays of Humboldt Current System: Insights from compound-specific isotopic compositions of amino acids

The Chilean upwelling bays are highly productive ecosystems shaped by their interactions with the open ocean. Although significant knowledge exists regarding their hydrodynamic and ecological processes, the spatial dynamics of trophic transfer and heterotrophic resynthesis of organic matter remain insufficiently understood. To address these knowledge gaps, we conducted a compound-specific isotope analysis of amino acids (CSIAA) on suspended and sinking particulate organic matter from Mejillones and Antofagasta bays, two oceanographic environments characterized by contrasting hydrodynamic conditions and topographic orientations. In Mejillones Bay, the CSIAA trophic positions for metazoan (1.7 ± 0.5) and protozoan (2.3 ± 0.3) were significantly higher compared to those in Antofagasta Bay (metazoans: 1.3 ± 0.6; protozoans: 1.5 ± 0.3), highlighting protozoans as primary trophic vectors. MixSIAR analysis indicated that phytoplankton is a key source of particulate organic matter in both bays; however, Mejillones Bay exhibited a greater proportion of microbially degraded organic matter. Enhanced heterotrophic resynthesis in Mejillones Bay (ΣV: 1.9–2.5) was associated with lower oxygen levels, increased concentrations of NO₂ ⁻ , and heightened stratification of the water column. Additionally, depth-dependent variations in δ 15 N for phenylalanine and threonine indicated a greater solubilization of particles, which contributed to a reduction in the export of particulate organic matter (averaging 9 ± 2 mg C/m²/d). These findings underscore the critical role of the intricate interactions between the bay’s topographic features and the physical and biological processes that ultimately influence the cycling trajectories of particulate organic matter in upwelling bays.

Leveraging stacking machine learning models and optimization for improved cyberattack detection

Correction to “Catalytic Asymmetric Oxidative Coupling between C(sp³)–H Bonds and Carboxylic Acids”

Hemostatic parameters in transgender women receiving gender-affirming hormone therapy: A shift to a cisgender female pattern?

Transgender women have an increased incidence of venous thromboembolism (VTE) compared with cisgender individuals. However, data on hemostatic parameters in this population are scarce. We aimed to evaluate hemostatic parameters in transgender women receiving gender-affirming hormone therapy (GAHT) compared with cisgender controls. We conducted a cross-sectional study including 40 transgender women (sample size based on prior calculation), and age- and body mass index-matched cisgender women (n = 25) and cisgender men (n = 25) as controls. Blood samples were collected between 2016 and 2023. We assessed hemostatic parameters (plasminogen activator inhibitor-1 [PAI-1], free protein S, vascular cell adhesion molecule-1, antithrombin, anticoagulant protein C, prothrombin time activity, thrombin time), hormonal profile (estradiol, sex hormone-binding globulin, estrogen dose, total testosterone, and free androgen index), and inflammatory markers (fibrinogen, C-reactive protein, and leukocyte count). Transgender women (mean [SD] age, 30.6 [8.0] years; median GAHT duration, 36.5 months) and cisgender women had similar hemostatic and inflammatory parameters. Compared with cisgender men, transgender women had higher PAI-1 levels (p = 0.001) and lower free protein S levels (p = 0.023). No differences were found in other hemostatic parameters between the groups. In conclusion, transgender women on long-term GAHT had higher levels of PAI-1 and lower levels of free protein S than cisgender men, indicating a slightly more prothrombotic profile. However, their hemostatic and inflammatory parameters were similar to those of cisgender women, suggesting a shift towards a female pattern. Factors beyond GAHT may contribute to the increased risk of VTE in this population.

All identical objects reduce memory load at the late maintenance stage in working memory

Abstract Visual working memory (VWM) is critical for temporarily storing and manipulating visual information but is limited in capacity. Previous research suggests that perceptual organization, such as grouping identical objects, may alleviate VWM resource constraints, yet the underlying mechanisms remain unclear. This study investigated whether identical orientation stimuli reduce VWM resource consumption and whether this reduction occurs during encoding or maintenance phase. Using the contralateral delay activity (CDA), an event-related potential (ERP) component that indexes the number of items stored in VWM, we examined three memory conditions: all-same (i.e., four identical orientations), partial-same (i.e., two pairs of identical orientations), and all-different (i.e., four different orientations) conditions. Behavioral results showed improved VWM performance as the number of identical orientations increased, with the highest performance in the all-same condition. ERP results revealed no significant differences in early CDA amplitudes across conditions, suggesting that identical stimuli do not reduce VWM resource consumption during early maintenance phase. However, late CDA amplitudes were significantly reduced in the all-same condition compared to the partial-same and all-different conditions, indicating that the benefits of identical objects emerge during the late maintenance phase. Notably, partially identical stimuli did not reduce VWM load, highlighting the complexity of integrating partially redundant information. These findings highlight the distinct contributions of early consolidation and maintenance phases in optimizing VWM through perceptual grouping and provide evidence that the facilitative effect of identical objects relies on their complete repetition and late-stage cognitive processes.

Correction to “Hexatopic Vertex-Directed Approach to Vinylene-Linked Covalent Organic Frameworks with Heteroporous Topologies”

Advancing patient care: Machine learning models for predicting grade 3+ toxicities in gynecologic cancer patients treated with HDR brachytherapy

Background Gynecological cancers are among the most prevalent cancers in women worldwide. Brachytherapy, often used as a boost to external beam radiotherapy, is integral to treatment. Advances in computation, algorithms, and data availability have popularized the use of machine learning to predict patient outcomes. Recent studies have applied models such as logistic regression, support vector machines, and deep learning networks to predict specific toxicities in patients who have undergone brachytherapy. Objective To develop and compare machine learning models for predicting grade 3 or higher toxicities in gynecological cancer patients treated with high dose rate (HDR) brachytherapy, aiming to contribute to personalized radiation treatments. Methods A retrospective analysis was performed on gynecological cancer patients who underwent HDR brachytherapy with Syed-Neblett or Tandem and Ovoid applicators from 2009 to 2023. After applying exclusion criteria, 233 patients were included in the analysis. Dosimetric variables for the high-risk clinical target volume (HR-CTV) and organs at risk, along with tumor, patient, and toxicity data, were collected and compared between groups with and without grade 3 or higher toxicities using statistical tests. Seven supervised classification machine learning models (Logistic Regression, Random Forest, K-Nearest Neighbors, Support Vector Machines, Gaussian Naive Bayes, Multi-Layer Perceptron Neural Networks, and XGBoost) were constructed and evaluated. The training process involved sequential feature selection (SFS) when appropriate, followed by hyperparameter tuning. Final model performance was characterized using a 25% withheld test dataset. Results The top three ranking models were Support Vector Machines, Random Forest, and Logistic Regression, with F1 testing scores of 0.63, 0.57, and 0.52; normMCC testing scores of 0.75, 0.77, and 0.71; and accuracy testing scores of 0.80, 0.85, and 0.81, respectively. The SFS algorithm selected 10 features for the highest-ranking model. In traditional statistical analysis, HR-CTV volume, Charlson Comorbidity Index, Length of Follow-Up, and D2cc - Rectum differed significantly between groups with and without grade 3 or higher toxicities. Conclusions Machine learning models were developed to predict grade 3 or higher toxicities, achieving satisfactory performance. Machine learning presents a novel solution to creating multivariable models for personalized radiation therapy.

Corallith bed of the endangered coral Cladocora caespitosa in the South Adriatic Sea

Abstract The colonial coral Cladocora caespitosa is a Mediterranean-endemic scleractinian structuring ecosystems of great conservation and ecological importance. Their nomenclature is here revised distinguishing between three different C. caespitosa formations: beds, grounds and reefs. Here we describe an extensive C. caespitosa bed at Tremiti Islands (Italy, Adriatic Sea), where thousands of colonies develop as free-living coral nodules (coralliths) on a soft, detritic bottom. Colonies were seldom present shallower than 15 m depth as well as on rocky bottoms, while their density increased with depth up to 700 colonies 100 m− 2 at 22 m. Their bathymetric occurrence was primarily influenced by seawater temperatures, particularly during summer, when a marked thermocline act as protection against high temperatures. The conservation status of the C. caespitosa bed was overall good, with low epibiosis/necrosis, and negligible bleaching. Demographic traits and size-structure of both colonies and polyps (through corallites) are reported. These rare coral formations might be affected by anthropogenic threats at both local and global scale, thus appropriate conservation measures are needed to avoid significant impacts in the future.

Double-Walled Mesoporous Hydrogen-Bonded Organic Frameworks with High Methane Storage Capacity

Just another bike? Modelling the interdependence of conventional and electric bicycle ownership and the influence of topography using large-scale travel survey data from Germany

Electrical bicycle ownership rates are growing rapidly. Despite differences to conventional cycling, the two types of bicycles are generally not differentiated in travel demand modelling practice. This article analyses the choices to own electric and conventional bicycles in Germany at the personal level. We use data from the “Mobility in Germany” survey and other sources and estimate both a nested logit model and a multivariate probit model. While the average gradient of terrain near the residence has an expected, strong negative influence on the ownership of conventional bicycles, electric bicycle ownership is much less negatively affected. The effect of socio-demographic variables is largely in line with that of the existing literature. A negative correlation of the error terms in the probit model indicates a substitutive relationship between the two ownership decisions. The high nest parameter value in the nested logit model indicates that the decision to own a conventional bicycle is secondary to the decision to own an electric bicycle. The results contribute to a better understanding of the motivations for or against bicycle ownership and create a basis for better consideration of electrical bicycle traffic in transport models.