Search Results (5,478)

Scaffolds designed for mechanically demanding soft tissue engineering applications should integrate mechanical support, efficient mass transfer, and good cellular compatibility. This work presents a one-pot method based on “radical-free click chemistry + carbodiimide coupling” to produce a double-network (DN) PEG–alginate cryogel. The PEG network is formed by a Michael addition reaction between thiol-based crosslinker and 8-arm PEG-acrylate. The second network is covalently crosslinked through EDC/NHS-mediated coupling of carboxyl groups in alginate and adipic acid dihydrazide (AAD). The subsequent freezing and gelation of the gel precursor at sub-zero temperatures results in an ice templated cryogel with an interconnected macroporous network. These cryogels demonstrate high elasticity, compressive modulus and rapid swelling equilibrium in aqueous environments, as well as controlled degradation under physiological conditions. Compared to the classical Ca²⁺ ion crosslinking systems, the covalent linking of the alginate in the double-network cryogel shows advantages in mechanical and structural stability. In addition, it is cell-compatible and allows culture of mesenchymal stem cells (MSCs) with homogeneous infiltration. Furthermore, the double-network cryogels supports chondrogenic differentiation of MSCs upon treatment with chondrogenic media or macrophage-conditioned media for a short period of time. These results indicate that crosslinking chemistry and polymer composition can be used to modulate the balance between mechanical performance and degradation behavior, while maintaining cytocompatibility and an interconnected macroporous network, thereby providing a scaffold design strategy for applications that require coordinated mechanical support and mass transfer, such as cartilage-related tissue engineering. Full article

Aortic stenosis is predominantly treated through transcatheter bioprosthetic heart valve implantation. However, the materials used in these devices are prone to premature failure. Polymer heart valves provide an alternative to current commercial devices, offering materials with greater durability and customisation through fibre reinforcement. Given the wide range of available materials and structures, there is a need for a systematic and efficient approach to designing and optimising novel bioinspired polymeric leaflets. This work presents a framework that employs computational modelling and Design of Experiments (DOE) tools to optimise bioinspired, 3D-printed, fibre-reinforced polymer leaflets made using melt electrowriting (MEW). Here, finite element (FE) models are created to represent MEW fibre-reinforced polymer leaflets for application in a transcatheter aortic heart valve. The behaviour of this valve under physiological loading conditions is modelled to predict valve performance and leaflet material response. These models were first used to investigate the impact of fibre orientation on valve performance and leaflet response, thereby demonstrating the benefits of a bioinspired fibre reinforcement structure. Using a DOE approach, the structural combination of MEW fibre reinforcement and an elastomeric matrix was optimised to improve valve performance and reduce leaflet stress and strain. Overall, the framework offers an efficient and versatile methodology for optimising fibre-reinforced polymer leaflets using an in silico approach, thereby reducing the need for physical prototyping and testing of these next-generation devices during early product development. Full article

In rocky mountainous regions characterized by shallow, barren soils and water scarcity, non-rainfall water, such as condensation, plays a crucial ecological role in mitigating seasonal drought in forest trees. To enhance the water-use capacity of vegetation, this study utilized a previously developed eco-friendly PVA–CS/SA–Ca²⁺ hydrogel. The primary objective was to elucidate the synergistic mechanisms by which the hydrogel optimizes condensed water utilization and drives the ecophysiological recovery of Pinus tabuliformis and Platycladus orientalis, two keystone afforestation species in northern China. Utilizing a controlled environmental chamber to simulate the condensation and humidification process, the experiment established three treatments: a control group (CK), a pot-sealed group (PS, to isolate soil water absorption), and a hydrogel-amended group (Hydrogel-Root Wrapping, HRW). To comprehensively evaluate the water utilization mechanisms, the amount of condensed water captured by the system was quantified, and hydrogen isotope tracing techniques were employed to precisely track water transport pathways and contribution rates. Concurrently, key physiological parameters were systematically determined, including leaf water potential, stomatal conductance, leaf water content, net photosynthetic rate, and transpiration rate. The results demonstrated the following: (1) the hydrogel significantly enhanced the condensation water capture capacity of the system. The net mass gains of the Pinus tabuliformis and Platycladus orientalis systems under the HRW treatment reached 26.3 g and 32.9 g, respectively, which represented 1.17 and 1.30 times those of the CK treatment, and 1.52 and 1.54 times those of the PS treatment. (2) Isotope tracing confirmed that both tree species possess significant Foliar Water Uptake (FWU) capacity. Following condensation, the δ²H values in the leaves of Platycladus orientalis and Pinus tabuliformis surged to 113.5‰ and 85.3‰, respectively, with stem δ²H values increasing by 31‰ and 22‰ compared to their initial baseline. (3) The introduction of the hydrogel in the HRW treatment provided 11.2% and 10.9% of the stem water supply for Platycladus orientalis and Pinus tabuliformis, respectively, thereby reducing their dependence on soil water by 8.3% and 13.1%. In contrast, there was no significant difference in the fractional contribution of condensation water to stem water between the PS and CK treatments. (4) Regarding physiological responses, the application of the hydrogel material effectively improved the physiological status of the plants. The leaf water potentials of Pinus tabuliformis and Platycladus orientalis increased to −0.15 MPa and −1.32 MPa, respectively. Concurrently, stomatal conductance (3.25 and 3.64 mm·s⁻¹) and leaf water content (58.4% and 67.4%) were significantly higher than those in the other treatments. In summary, the hydrogel can significantly enhance the capture, conversion, and utilization efficiency of condensation water by vegetation, effectively optimizing the water supply dynamics of the system. This provides key theoretical and technical support for ecological afforestation in difficult sites within rocky mountainous areas. Full article

Tropospheric ozone (O₃) is a phytotoxic air pollutant that can impair visible foliar injury (O₃ VFI) and reduce photosynthesis in sensitive forest species. Viburnum lantana L. has been used as an in situ bioindicator of O₃ pollution in mountainous areas of Europe; however, flux-based response functions and critical levels (CLs) for this species have not yet been established. This study validated field-observed O₃ effects in V. lantana through experiments carried out in a Free-air O₃ eXposure infrastructure and determined which O₃ metric (exposure-based AOT40 or flux-based-POD₁) best explains O₃ effects on leaf physiology and VFI. Throughout the experimental period (T2: 3.5-month O₃ exposure), V. lantana saplings were subjected to ambient air (AA) conditions and elevated O₃ levels (1.5× and 2.0× AA). O₃ VFI appeared after 16 days in 2.0× and increased progressively during the growing season, reaching the highest Plant Injury Index (PII) values in the 2.0× (9.06 ± 3.24) compared with 1.5× and AA treatments (1.31 ± 0.62 and 1.29 ± 0.71). Elevated O₃ also significantly reduced net photosynthetic rate (A_sat), relative chlorophyll content (SPAD), and the maximum photochemical efficiency of photosystem II (F_v/F_m); no significant difference in stomatal conductance (g_s) was found. The flux-based metric POD₁ better explained variability in O₃ VFI and physiological parameters. Based on the best-fitting models, CLs for V. lantana were estimated at 1.61 mmol m⁻² and 1.22 mmol m⁻² for a 4% reduction in A_sat and g_s, and a CL of 7.82 mmol m⁻² for the O₃ VFI-onset. Full article

The growing use of wireless technology significantly increases the exposure of all living organisms to radiofrequency electromagnetic fields (RF-EMF). However, the physiological effects of RF-EMF on plants have not yet been sufficiently researched. In this study, we investigated the effects of RF-EMF radiation in the frequency ranges 1890–1900 MHz (DECT) and 2.4 GHz plus 5 GHz (Wi-Fi) on photosynthetic performance of Tilia plants (Tilia tomentosa). The recorded fast chlorophyll fluorescence transients were used to analyze the structure and function of PSII by the JIP-test. The analysis of the fluorescence of chlorophyll a showed that the RF-EMF interfered with the electron transport processes of photosynthesis. Tilia plants exposed to RF-EMF induced decrease in photosynthetic efficiency (F_V/F_M) and inactivation of part of PSII reaction centers (RC/CS_O). Observations of leaf senescence and lifespan over a period of 102 days showed that RF-EMF-exposed Tilia plants exhibited accelerated aging. Full article

Non-invasive fetal electrocardiogram (FECG) extraction from maternal abdominal ECG (AECG) is crucial for prenatal monitoring but remains challenging due to strong interference from maternal ECG (MECG), baseline drift, and noise. We propose an FECG extraction method based on minimal channel attention (MCA) and flow matching (FM), learning a deterministic mapping from AECG to FECG via a probabilistic path. To balance the preservation of physiological signals and separation of interference, we employ bridge variance scheduling for the diffusion process. Target matching loss is introduced to regress the FECG directly, enhancing training stability and waveform fidelity. For feature selection, a minimal channel attention module with global average pooling and a single linear layer is embedded after feature extraction, capturing cross-channel dependencies with minimal parameters. Enhanced residual connections are incorporated to retain underlying features and optimize gradient flow in deep networks. Experiments on two public datasets (ADDB and BDDB) with a leave-one-out cross-validation strategy show that our method achieves average Pearson correlation coefficients (PCCs) of 0.94 ± 0.050 on ADDB and 0.91 ± 0.122 on BDDB, demonstrating robust performance across diverse real-world recording conditions. The method balances high accuracy with efficient feature extraction, offering a reliable solution for non-invasive fetal heart health monitoring. Full article

Studies aimed at identifying genotypes tolerant to water deficit are essential for the development of superior plant materials adapted to regions with limited water availability, such as the Brazilian Semi-Arid. This study evaluated the physiological, biochemical, and enzymatic responses of Stylosanthes spp. subjected to different levels of water availability (60%, 40%, and 20% of pot capacity). The experiment was conducted using a completely randomized design using a 3 × 2 factorial scheme, comparing the accession BGF 11-001 and the cultivar BRS-Bela (cv. Bela). Physiological traits, biochemical variables, and antioxidant enzyme activity were analyzed. The accession BGF 11-001 showed resilience under water deficit, maintaining high chlorophyll content even under severe stress. This response was associated with increased accumulation of amino acids such as proline, as well as enhanced antioxidant activity, indicating a tolerance mechanism based on osmotic adjustment and cellular protection. In contrast, cv. Bela exhibited higher sensitivity to water stress, with a pronounced reduction in photosynthetic pigments and greater accumulation of compatible solutes, including total soluble proteins, reducing sugars, amino acids, and proline, without significant activation of antioxidant enzymes. Overall, the results demonstrate that the genotypes adopt distinct strategies to cope with water stress, with BGF 11-001 being more efficient in activating defense mechanisms. Therefore, BGF 11-001 has agronomic potential for cultivation in drought-prone regions and is a promising genetic resource for forage breeding programs aimed at improving drought tolerance. Full article

Living wall systems are increasingly used in indoor environments as elements of biophilic design; however, plant growth in these systems relies almost entirely on artificial lighting. While light-emitting diode (LED) technology offers flexible spectral properties, limited information is available on how commercially available white LEDs with different correlated color temperatures (CCTs) affect plant performance in indoor vertical greenery systems. The present study evaluated the effects of three white LED lamps differing in CCT, specifically warm-white (2700 K), neutral-white (4000 K), and cool-white (6500 K), on the growth, flowering, physiological performance, and visual perception of Spathiphyllum wallisii Regel cultivated in an indoor living wall system under exclusively artificial lighting. Plants were grown for eight months under a 12 h photoperiod, and growth parameters, flowering, SPAD index, photosystem II efficiency (F_v/F_m), and biomass accumulation were assessed. In addition, a questionnaire survey evaluated visual preferences under the different lighting conditions. Plant growth parameters, flowering, and physiological performance were largely unaffected by CCT, indicating that S. wallisii can adapt to a wide range of white LED spectra under low-irradiance conditions (~16–18 μmol m⁻² s⁻¹). However, cool-white lighting was associated with slightly higher canopy coverage and aboveground biomass compared to other treatments, although overall differences among CCTs were relatively limited. In contrast, survey results indicated a clear preference for neutral-white lighting, which was most frequently perceived as providing the most natural plant appearance and the most suitable illumination for indoor living walls. These findings suggest that neutral-white LEDs may provide a suitable balance between maintaining satisfactory plant growth and achieving favorable visual perception in indoor greenery installations. Full article

Optimizing the interaction between planting density and nitrogen (N) application rate is critical for simultaneously improving grain yield and nitrogen use efficiency (NUE) in winter wheat (Triticum aestivum L.). However, the underlying regulatory mechanism remains poorly understood in the fluvo-aquic soil region of the southern Huang–Huai–Hai Plain. This study aimed to elucidate the physiological mechanism by which planting density and nitrogen application interactively regulate source–sink coordination to achieve synergistic high grain yield and high NUE, and to screen the optimal local cultivation combination for winter wheat in southeastern Henan. A two-year consecutive field experiment was conducted from 2018 to 2020 in Shangshui, Henan, using a split-plot design. Three planting densities (D1: 225 × 10⁴ plants ha⁻¹; D2: 375 × 10⁴ plants ha⁻¹; D3: 525 × 10⁴ plants ha⁻¹) and five N rates (N0: 0; N1: 180; N2: 240; N3: 300; N4: 360 kg N ha⁻¹) were established. Results demonstrated that planting density, N rate, and their interaction significantly regulated grain yield, NUE, and dry matter and N allocation, with consistent trends across both years. Increasing density enhanced total biomass and N accumulation, but dry matter and N partitioning to grains declined when density exceeded 375 × 10⁴ plants ha⁻¹. Grain yield exhibited a quadratic response to N rate; the optimal N rate for maximum yield decreased from 296.33 kg ha⁻¹ at low density (D1) to 237.50–245.38 kg ha⁻¹ at medium and high densities. The combination of 240 kg N ha⁻¹ and 375 × 10⁴ plants ha⁻¹ (D2N2) produced the highest average grain yield (8875.35 kg ha⁻¹), with simultaneous improvements in spike number and kernels per spike as well as superior dry matter and N partitioning to grains. This combination also maintained high nitrogen recovery efficiency (NRE) and nitrogen agronomic efficiency (NAE). Correlation analysis revealed that grain yield and NUE were significantly positively correlated with dry matter accumulation, N accumulation, and their partitioning proportions to grains. Overall, D2N2 achieved simultaneous high yield and high NUE by coordinately optimizing dry matter and N partitioning to grains. We therefore recommend reducing N fertilizer to approximately 240 kg ha⁻¹ combined with a moderate planting density of 375 × 10⁴ plants ha⁻¹ as the preferred strategy for sustainable and intensive winter wheat production in the fluvo-aquic soil region of southeastern Henan and adjacent areas. Full article

Background: Uncontrolled hemorrhage, especially at non-compressible sites, remains a major cause of preventable trauma deaths. This study reports the development of fibrinogen-loaded calcium carbonate (CaCO₃) microparticles that combine hemostatic activity with self-propelling capability for targeted delivery against blood flow, with a focus on understanding formulation-dependent trade-offs among particle yield, protein loading, clotting performance, and transport behavior. Methods: Microparticles were synthesized via a precipitation method using different carbonate sources and characterized for yield, morphology, size, and fibrinogen encapsulation. Hemostatic function was assessed using rotational thromboelastometry (ROTEM) in fibrinogen-deficient plasma. Propulsion behavior was evaluated following exposure to protonated tranexamic acid (TXA⁺), which triggers CO₂ generation. Particle size and encapsulation were examined by microscopy and fluorescence imaging. Results: The precipitation method produced spherical micrometer-sized particles, with fibrinogen inclusion reducing yield and particle size relative to unload controls. Fluorescence microscopy confirmed successful encapsulation. Encapsulation efficiency varied with formulation, with sodium carbonate-based particles showing higher relative fibrinogen loading. ROTEM analysis demonstrated that fibrinogen-loaded particles significantly improved clot formation, increasing maximum clot firmness compared to fibrinogen-free particles, although performance remained formulation-dependent. TXA⁺-triggered propulsion achieved maximum speeds up to 4.221 cm/s. Fibrinogen-loaded particles exhibited longer activation lag times than unloaded particles, indicating a trade-off between hemostatic functionality and propulsion kinetics. Conclusions: Fibrinogen-loaded CaCO₃ microparticles exhibit both hemostatic activity and chemically triggered motion in vitro. The study identifies key formulation-dependent trade-offs between particle yield, fibrinogen loading, clotting performance, and propulsion behavior. While these findings support the feasibility of combining localization and clot stabilization mechanisms, further studies under physiologically relevant flow conditions and in vivo models are required to evaluate their potential for active delivery in non-compressible hemorrhage. Full article

Sunflower (Helianthus annuus) can potentially be used for uranium (U) phytoremediation. However, the influence of arbuscular mycorrhizal fungi (AMF) on key rhizosphere processes and plant U uptake remains insufficiently researched. We hypothesized that AMF inoculation could enhance sunflower tolerance to U stress by improving plant physiological performance and modifying rhizosphere properties. To test this hypothesis, this study examined the effects of AMF (Funneliformis mosseae, Glomus etunicatum, and their co-inoculation) on sunflowers under U stress, encompassing plant growth and physiological traits, rhizosphere properties, enzyme activities in the rhizosphere soil, uranium speciation in the rhizosphere soil, and the accumulation and distribution of uranium within the plant. Results showed that AMF successfully colonized the roots, enhancing plant growth, biomass, and gas exchange, while improving photosynthetic efficiency and reducing non-photochemical quenching. In the rhizosphere, AMF elevated soil respiration, organic matter, dissolved organic carbon, and microbial biomass carbon; improved phosphatases, urease, catalase, and sucrase activities; also reshaped U speciation, increasing exchangeable and carbonate-bound fractions while decreasing those bound to organic matter, Fe/Mn oxides, and residual phases. Moreover, AMF reduced U concentration in leaves and stems, promoted U retention in belowground tissues, and significantly lowered the U translocation factor. These findings demonstrate that AMF inoculation improves sunflower tolerance to U stress by enhancing physiological performance, modifying rhizosphere properties, and immobilizing U in roots, supporting its potential use in phytoremediation strategies for U-contaminated environments. Full article

Preharvest climatic conditions and irrigation management are decisive determinants of avocado postharvest performance. Avocado trees are highly susceptible to the water regimen, conditions that disrupt carbon assimilation, mineral nutrient uptake, and biomass partitioning. This study evaluated the effects of deficit irrigation imposed during early stages of fruit growth, coinciding with active cell division, on fruit development and postharvest quality of ‘Hass’ avocado. Deficit and excess irrigation induced physiological stress, reducing stem water potential (≈−1 MPa) and altering photochemical efficiency, while F_V/F_M remained unaffected. Fruit growth was strongly affected, with weight reductions of up to 26% during development and 22% at harvest under severe deficit, resulting in fruits becoming more yellowish-green. In contrast, excessive irrigation promoted larger fruit with darker green skin, with delayed maturation. Metabolomic revealed that the fruit developmental stage was the main driver of metabolic variation, while irrigation effects were minor and stage-dependent, limited to osmotic-related metabolites such as GABA. These findings indicate that early-season water imbalances primarily affect fruit growth through changes in water relations rather than metabolic reprogramming, highlighting the importance of precise irrigation management during critical developmental stages. Fine-tuning water supply during early developmental stages is a strategic tool for optimizing fruit size and postharvest quality in avocado. Full article

Reliable monitoring of spontaneous affect is essential in biomedical sensing, where involuntary facial signals serve as objective indicators of physiological states. Micro expression recognition (MER) is particularly challenging due to the sub-second, low amplitude nature of these signals. Many existing MER methods rely on apex (peak) frame detection, making them sensitive to temporal localization errors and difficult to deploy in unconstrained settings. To address this, we propose an apex-free framework that analyzes facial dynamics by structuring motion-magnified features along a newly introduced magnification intensity axis. By applying Eulerian motion magnification across multiple discrete levels and collapsing the sequences into single accumulation images, we generate a multi-level representation of subtle facial dynamics without requiring frame-level annotations. A lightweight shared temporal mixer (STM) is employed to analyze the dynamic evolution of motion across the magnification intensity axis. Subsequently, a dual-branch convolutional neural network (CNN), processing low- and high-amplification regimes respectively, integrates a convolutional block attention module (CBAM) to capture subtle facial motion while effectively filtering out irrelevant noise. Our model is highly efficient, requiring only 0.94 M parameters and 262 MFLOPs, which is significantly lower than the computational demands of standard backbones such as ResNet18 or VGG16. To ensure the model generalizes to new individuals, we evaluated it by testing on subjects whose data was entirely excluded from the training process. Under this rigorous setup, the proposed method achieves approximately 80% and 70% accuracy on the CASME II and SMIC datasets respectively, showing performance comparable to, or in some cases, slightly above current state-of-the-art methods. Considering both the competitive accuracy and high computational efficiency, the proposed framework holds significant potential for practical integration into real-time affect monitoring systems, particularly within biomedical applications. Full article

Emotion recognition plays a crucial role in human–computer interaction, health monitoring, and affective computing by analysing physiological signals. Despite recent advancements, current research still faces challenges, including the lack of effective fusion strategies for diverse physiological modalities, difficulties in handling high-dimensional feature representations, and limited use of efficient temporal modelling techniques to capture complex emotional patterns. This study proposes a deep learning-based approach that fuses multiple physiological modalities, including Electroencephalography (EEG), Electrooculography (EOG), Electromyography (EMG), Galvanic Skin Response (GSR), Respiratory Rate (RR), Skin Temperature (SKT), and Photoplethysmography (PPG), to improve emotion recognition. Arousal and valence ratings were binarized into two classes (low/high) using a threshold of 4.5, formulating a binary classification problem. In addition to utilising Bidirectional Long Short-Term Memory (Bi-LSTM), the study employs Temporal Convolutional Networks (TCN), a widely used approach for time-series analysis, to efficiently capture temporal dependencies. The proposed model optimises feature selection through channel-wise strategies, incorporates advanced learning rate scheduling, and reduces computational overhead. Furthermore, window-wise, block-wise, and trial-wise evaluation protocols were investigated to assess the impact of temporal information leakage on emotion recognition performance. Using the DEAP dataset for validation, the proposed TCN-based approach achieved classification accuracies of 88.42% for valence and 86.35% for arousal under an overlapping block-wise evaluation protocol, demonstrating improved performance in binary emotion recognition and highlighting the importance of leakage-aware model assessment. Full article

Background: Continuous Positive Airway Pressure (CPAP) therapy is the standard treatment for obstructive sleep apnea–hypopnea syndrome (OSAHS), and photoplethysmography (PPG) sensors are commonly used in wearable devices for home sleep apnea testing. The recorded airflow and PPG signals from both sensors capture rich physiological patterns. We hypothesize that by combining information from these signals, we can efficiently estimate sleep dynamics of patients receiving CPAP treatment. Methods: The airflow signals were obtained from CPAP titration devices, denoted as CPAP-airflow, while the PPG signals were collected using the PranaQ TipTraQ (TTQ001), a fingertip-worn wearable device. We separately trained one-dimensional convolutional neural networks for CPAP-airflow and PPG signals and fused their outputs through probabilistic ensembling to predict sleep stages. The ensemble method is a late-fusion soft-voting scheme that computes a linearly weighted combination of synchronized softmax probability vectors from the modality-specific models. Results: For three-stage classification (Wake, REM, NREM), the PPG-based and CPAP-airflow-based models achieved overall Cohen’s kappa scores of 0.511 and 0.452, respectively, while the ensembled model improved the overall kappa to 0.587. The F1-score for the REM stage improved to 0.706 using the ensemble method, compared to 0.685 and 0.532 achieved by the individual models, respectively. In the four-stage classification (Wake, REM, Light, Deep) task, a deep sleep sensitivity of 0.596 was attained through the application of probabilistic ensembling. Conclusions: A fusion scheme of complementary information from the CPAP and PPG enhances the accuracy of sleep stage detection and hence enables more precise sleep monitoring, especially with an improved REM identification. Clinical implications include applying the proposed algorithm to improve in-home auto-CPAP titration by capturing REM-related respiratory instability and avoiding under-titration in REM-dominant OSAHS, better reflecting the patient’s true nocturnal respiratory needs. Full article