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Tetrazine-Mediated Bioorthogonally Activated Therapeutic (TBAT) Platforms
Reception of conspecific cues alters testicular gene expression and improves fertility in boreal chorus frogs (Pseudacris maculata)
Abstract Evidence for the social modulation of reproduction in vertebrates is established but poorly defined for many taxa despite the link between social cues and endocrine function. It is unclear if physiological effects, such as increased sex hormone concentration or gamete maturation, impact reproductive outcomes, such as fertilization rates or viability of offspring. We investigated the effect of conspecific signals on the calling behaviour, reproductive output, and gonadal gene expression in the boreal chorus frogs ( Pseudacris maculata ). Using breeding trials in a captive setting, we found that broadcasts of conspecific calls nearly doubled the duration of calling activity and increased the proportion of viable eggs by over 13%. We then used RNA-sequencing techniques to demonstrate exposure to broadcasts of chorus behaviour increased the expression of genes associated with steroidogenesis, spermatogenesis, and gonadal development in the male chorus frogs within 6 h. Several gene pathways were similarly affected in the male chorus frogs receiving hormone injections to induce sperm development and release, implying there are parallels in hypothalamic-pituitary-gonadal axis stimulation induced by reception of conspecific cues and hormonal induction. We provide some of the first evidence of amphibian social signals causing rapid molecular changes linked to fertility regulation.
Hemodynamic simulation in the aortic arch under anemic, diabetic, and healthy blood flow conditions using computational fluid dynamics
This study examines the hemodynamic impact of anemic, diabetic, and healthy blood conditions in the human aortic arch using computational modeling. Blood rheology was represented by the shear-thinning Carreau–Yasuda model, and simulations were carried out in a patient-inspired aortic geometry under pulsatile flow. Velocity fields, pressure gradients, and wall shear stress (WSS) distributions were quantified to assess how altered hematocrit and viscosity affect vascular loading. Anemic blood, characterized by low viscosity, showed smooth low-resistance flow with reduced WSS, potentially limiting endothelial stimulation and impairing perfusion. Diabetic blood exhibited elevated viscosity and hematocrit, producing higher flow resistance, increased WSS, and disturbed secondary flows, consistent with vascular stiffening and remodeling risk. Healthy cases maintained balanced hemodynamics within physiological ranges. These findings highlight the mechanistic links between blood rheology and vascular stress, offering non-invasive insights for risk stratification in hematological and metabolic disorders, and supporting the integration of CFD-based analysis into clinical decision-making.
Unraveling the Role of Ionizable Lipid Isomerism in Modulating Lipid Nanoparticles for mRNA Delivery
Polarity-resolved far-side magnetograms based on helioseismic measurements
Abstract Understanding and monitoring solar active regions is essential for operational space-weather forecasting and better solar dynamo modeling. This requires comprehensive 360 $$^\circ$$ observations of the Sun. While space-weather forecasting has long relied successfully on high-quality observations of the Earth-facing hemisphere, a critical gap in global magnetic context remains due to the lack of direct, continuous magnetic field measurements of far-side active regions, specifically magnetic field strength, polarity configurations, and related parameters. We present a methodology for inferring magnetic field distributions of active regions in helioseismic maps of the far hemisphere. The crux of the analysis is the ability to realistically surmise the signs of the magnetic polarities of opposing components of a helioseismic signature. We present a method for stable, continuous polarity assignment of large-scale magnetic structures, derived from substructures that helioseismic signatures reliably resolve in strong active regions–particularly those that become space-weather hazards as solar rotation brings them into Earth’s view. Polarity boundaries are identified by analyzing the bi-modal longitudinal variance profile of the seismic signal within each region, after which Hale’s polarity rule is applied to establish east–west ordering consistent with the solar cycle. The method yields polarity-resolved far-side magnetograms that are suitable for integration with near-side observations, enabling the construction of full-Sun magnetic boundary conditions for coronal and solar-wind modeling, and providing a critical step toward improved heliospheric simulations and operational forecasting.
Effects of interactivity, immersion, and physical discomfort on learning in VR nursing education
Background Although VR provides innovative opportunities for nursing education, little is known about how its core features immersion and interactivity shape learners’ self-efficacy, motivation, and cognitive processes. Furthermore, the impact of physical discomfort during VR use remains underexplored. This study investigates the effects of immersion and interactivity on affective and cognitive constructs in nursing education using virtual reality (VR), while examining the influence of physical discomfort and the moderating roles of age and gender. Methods A cross-sectional study was conducted with 209 students (aged ≥20 years) recruited from three nurse aide training centers in Taiwan. The research team developed a VR training system using Unity 3D and Autodesk 3DS Max, incorporating three essential nursing skills: Heimlich maneuver, meal preparation/feeding/medication assistance, and vital sign measurement. After completing the VR training, participants filled out structured questionnaires. Data were analyzed using partial least squares structural equation modeling (PLS-SEM). Results Interactivity showed stronger positive associations than immersion with self-efficacy, intrinsic motivation, situational interest, and embodied learning, and was associated with lower levels of extraneous cognitive load. Visual discomfort was negatively associated with both immersion and interactivity, whereas neck, shoulder, and back (NSB) pain was positively associated with immersion. Multi-group analysis revealed that female participants reported greater visual discomfort, which reduced immersion and interactivity. Additionally, participants under 35 years old exhibited greater sensitivity to visual discomfort in relation to immersion compared with older participants. Conclusions Interactivity is more crucial than immersion for enhancing affective and cognitive learning outcomes in VR-based nursing education. Visual discomfort significantly impairs learning experiences, with stronger negative effects among female and younger users, suggesting the need for further attention to user characteristics and physical comfort in VR-supported nursing education.
From Boron to Bismuth: The Pre-transmetalation Complex in the Aryl Transfer
Modeling and experimental analyses for Chitosan/Zinc oxide nanocomposite
Abstract The molecular structural and optical properties of chitosan (Cs) and its nanocomposites with zinc oxide (ZnO) are investigated using a combination of Density Functional Theory (DFT) calculations at the B3LYP/LANL2DZ level and experimental techniques (FTIR and UV-Vis diffuse reflectance spectroscopy). Different coordination modes (amine -NH₂, hydroxyl -OH, and oxygen-linkage) were modeled to describe Cs-ZnO interactions. The formation of Cs/ZnO and Cs/2ZnO complexes significantly increases the total dipole moment (TDM) from 5.884 Debye in pure Cs to 14.049 Debye in Cs/2ZnO via O-linkage and reduces the HOMO/LUMO energy gap (ΔE) from 6.908 eV in pure Cs to 2.239 ± 0.05 eV in Cs/2ZnO via OH indicating enhanced polarity, charge-transfer, and electronic reactivity. Global reactivity descriptors further confirm increased softness and electrophilicity upon ZnO incorporation. Electronic structure analyses (MESP, DOS/PDOS, and QTAIM) elucidate coordination and charge redistribution mechanisms. Experimentally, FTIR spectra reveal substantial interfacial interactions through shifts in N–H bending (from 1583 cm⁻¹ in pure Cs to lower wavenumbers with increasing ZnO content) and the appearance of Zn–O bands (424–600 cm⁻¹). UV-Vis diffuse reflectance spectroscopy and Tauc plot analysis show a redshift in optical bandgaps with rising ZnO content, attributed to defect states and band tailing; the direct bandgap decreases from 4.35 ± 0.05 eV (pure Cs) to 3.28 ± 0.04 eV (4 wt% ZnO), and the indirect bandgap from 3.19 ± 0.05 eV to 2.56 ± 0.04 eV. This study advances prior chitosan/ZnO research by providing comprehensive QTAIM-mapped analysis of specific Cs-ZnO binding modes (amine-H, O-linkage, OH) correlated with quantitative DFT reactivity descriptors, FTIR vibrational shifts, and defect-induced optical bandgap tuning in nanocomposites. These findings highlight the tunable electronic and optical properties of Cs/ZnO nanocomposites, positioning them as promising candidates for photocatalysis, optoelectronics, and sensors.
Expression of concern: HSV-2 regulates monocyte inflammatory response via the Fas/FasL pathway
Electrolysis-Assisted Reduction of Dimethylformamide for Unactivated Alkene Functionalizations
Submarine groundwater discharge and associated fluxes along the Kanyakumari coast of India using radon and nutrient mass balance approach
Abuse recognition by shelter staff and shelter animal adopters
Science has provided insight into the physical signs of animal abuse, such as the presence of fractures in different healing stages. Possible behavioural signs of animal abuse are understudied. In studies on child abuse, behavioural signs have been identified. We aimed to study if similar signs would be viewed as relevant signs of animal abuse, focussing on physical abuse of cats and dogs. We targeted shelter staff and shelter animal adopters, as these people may come in contact with abused animals, are known to be willing to participate in research and may do so without ethical constraints applying. We found that the behavioural signs based on child abuse studies, were deemed relevant for the assessment of animal abuse by the respondents in our small-scale study (N = 23 shelter staff and N = 132 shelter animal adopters). Behavioural signs of fear, such as fear of the owner, were deemed most relevant. Person-specific fear was indicated by shelter staff and shelter animal adopters as a more accurate indicator of animal abuse than aggression, problems with human-social bond formation and person-specific pleasing behaviour. This study held a limited number of respondents, but indicates a possible use of behavioural signs in studying animal abuse. To further assess the validity of animal behavioural signs as a source of animal abuse recognition, future studies will need to observe animals known to have previously suffered abuse. These studies should aim to use ethograms that include objective descriptions of behaviours that may be of interest to animal abuse experiences. Our study indicates that shelter animal staff and adopters may differ in their appraisal of behaviours other than fear, when assessing a likeliness of animal abuse. Studying such differences in larger samples may provide insight into how these populations assess an animal’s possible abuse experience or lack thereof.
A machine learning based scheme for enhancing the detection of position falsification attacks in vehicular ad hoc networks
Abstract Vehicular Ad Hoc Networks (VANETs) are wireless networks established between vehicles and their surrounding infrastructure, enabling the exchange of information. Consequently, many applications that can enhance passengers’ safety and traffic flow are built upon this information. However, malicious nodes can manipulate the exchanged data to attack other nodes and disrupt the network’s normal behavior. For example, if an attacker broadcasts a falsified location for a vehicle, the functionality of applications that rely on accurate location sharing will be compromised, potentially leading to deadly accidents. Although numerous Misbehavior Detection Schemes (MDSs) have been proposed to detect position falsification attacks, their effectiveness remains limited for certain attack types, raising concerns given the safety-critical nature of VANET applications. This paper proposes a machine learning-based method for detecting position falsification attacks. The proposed approach evaluates four machine-learning algorithms using three feature vectors (FV1, FV2, and FV3) composed of selected and derived features extracted from Basic Safety Messages (BSMs), in addition to a novel confidence-based Received Signal Strength Indicator feature, termed RSSIConf. The RSSIConf feature assesses the reliability of a sender’s claimed position by comparing the measured RSSI with confidence intervals corresponding to the claimed sender–receiver distance. Experimental results show that the Random Forest classifier trained with FV2 features achieves the best overall performance, outperforming existing approaches with improvements ranging from 0.76% to 13.26% in accuracy and from 0.74% to 12.71% in F1-score across different position spoofing attack types. These improvements enhance the reliability of misbehavior detection and contribute to safer and more trustworthy VANET communications.
Comparison of neonatal systemic and intracerebroventricular AAV9 gene therapy delivery demonstrating improved behavioral and phenotypic outcomes in a mouse model of Niemann-Pick disease, type C1
Niemann-Pick disease, type C (NPC), is an inherited fatal lysosomal storage disorder caused by a mutation in the NPC1 or NPC2 genes and characterized by impaired lysosomal cholesterol export. Previous studies have demonstrated that delivery of the NPC1 gene to the central nervous system (CNS) via an adeno-associated virus (AAV) can substantially improve lifespan and mitigate signs of disease in Npc1 -deficient mouse models of NPC. To determine the optimal parameters for an efficacious AAV-based gene therapy for NPC, we measured the survival and disease phenotypes of mice treated systemically as neonates or at weaning age, along with neonatal mice treated via intracerebroventricular (ICV) delivery, with a construct containing either a ubiquitous truncated EF1α promoter or a truncated Mecp2 promoter. While all constructs and delivery methods resulted in improvement compared with baseline, mice treated as neonates survived significantly longer and experienced slower disease progression compared with those treated systemically at weaning age. Systemic delivery to neonates was capable of increasing survival and phenotypic improvement comparable to that of ICV delivery, and neonatal systemic and ICV delivery were both similarly capable of near-total Purkinje cell rescue. We also found no difference between a ubiquitous EF1α-derived promoter and an Mecp2 -derived promoter. Ultimately, early treatment with maximal access to the CNS, whether via systemic or direct CNS delivery, is key to the efficacy of gene therapy in treating NPC.
Engineering Paramagnetic Spin Centers in Metal-Free Organic Frameworks
Design and thermo-structural analysis of multiple composite layers with ablative materials for passive thermal protection systems
Random subspace-based ensemble classifier for high-dimensional data Using SPARK
High-dimensional data classification remains challenging for machine learning models due to sparsity and overfitting caused by the ‘curse of dimensionality‘. As the number of features increases, data points become sparse, hindering generalization in classification and leading to higher computational costs and reduced accuracy. To address these issues, we propose an ensemble classifier based on random subspaces implemented in the Spark framework. The proposed framework comprises three key stages. First, the high-dimensional data is normalised through min-max normalisation. Second, the master node partitions the data by using improved deep fuzzy clustering (IDFC). In contrast, the slave node applies support vector machine-modified recursive feature elimination (SVM-MRFE) for efficient feature selection, followed by feature fusion. Finally, we introduced an improved subspace-based ensemble classifier (ISSBEC) that comprises a feature-fusion-based random subspace (FF-RSS), mixed-space enhancement (MSE), and multiple base classifiers. The efficacy of the ISSBEC classifier was evaluated using a set of performance metrics and compared with state-of-the-art methods. Experimental results demonstrate that the proposed approach improves both accuracy and robustness, offering a scalable solution to the limitations of high-dimensional datasets.
The time-dependent reliability of CRTS III slab ballastless track structures based on direct probability integral method
Lessons learned using species’ distribution models for conservation planning in the Golden Gate Biosphere reserve
Conservation practitioners responsible for maintaining biodiversity and ecosystem services within protected areas require information about how dominant plant species may reassemble under rapid global change. Although species’ distribution models (SDMs) alone do not account for multiple threats or species population dynamics, they can provide robust assessments of where species may persist or disperse to in the future, especially if carefully constructed and thoroughly evaluated. We used ensemble SDMs to evaluate how climate change may alter suitable habitat for six dominant plant species representing different life forms within the Golden Gate Biosphere Network (GGBN), located along the central to northern coast of California. We trained the models on presence-absence data and 23 environmental predictors, including climate, topography, and soils, using six algorithms. We projected habitat suitability to late-century climate conditions using three GCMs under RCP 8.5 and summarized areas of agreement, expansion, and refugia. Model results can be summarized into several lessons learned, many of which are consistent with previous research. The first is that projected habitat may either expand or contract, and the direction of change varies by individual species, even within the same genus. Many projected changes reflected species’ relationships with key climate variables relative to their future projected trends. Disagreement across scenarios was largely driven by uncertainty in projected precipitation changes, while non-climatic variables, particularly soils, were also important in mediating projected habitat change. Contrary to common assumptions, projected habitat shifts were not always upslope or poleward. Finally, although species face multiple threats from other global changes, SDMs can provide a valuable baseline for conservation decisions within small reserves, particularly through identification of refugia and comparison of model scenarios. However, habitat changes within protected areas may not reflect dynamics elsewhere across species’ ranges, underscoring the need for multi-scale conservation planning.