Search Results (497)

The aim of this work was to systematically evaluate the effects of Lion’s Mane Mushroom powder (LMM, 0–40%) on the physicochemical properties, structural characteristics, and flavor profile of soy protein isolate-based high-moisture meat analogues (HMMAs). Optimal incorporation of 20% LMM significantly enhanced product quality by acting as a secondary phase that inhibited lateral protein aggregation while promoting longitudinal alignment, achieving a peak fibrous degree of 1.54 with dense, ordered fibers confirmed by scanning electron microscopy. Rheological analysis showed that LMM improved viscoelasticity (G′ > G″) through β-glucan; however, excessive addition (≥30%) compromised structural integrity due to insoluble dietary fiber disrupting protein network continuity, concurrently reducing thermal stability as denaturation enthalpy (_ΔH) decreased from 1176.6 to 776.3 J/g. Flavor analysis identified 285 volatile compounds in HMMAs with 20% LMM, including 98 novel compounds, and 101 flavor metabolites were upregulated. The mushroom-characteristic compound 1-octen-3-ol exhibited a marked increase in its Relative Odor Activity Value of 18.04, intensifying mushroom notes. Furthermore, LMM polysaccharides promoted the Maillard reaction, increasing the browning index from 48.77 to 82.07, while β-glucan induced a transition in protein secondary structure from random coil to β-sheet configurations via intramolecular hydrogen bonding. In conclusion, 20% LMM incorporation synergistically improved texture, fibrous structure, and flavor complexity—particularly enhancing mushroom aroma. This research offers valuable insights and a foundation for future research for developing high-quality fungal protein-based meat analogues Full article

Inherited retinal dystrophies (IRDs) are clinically and genetically heterogeneous disorders. Most IRDs follow a monogenic inheritance pattern. However, an increasing number of unresolved cases suggest the possible contribution of oligogenic or digenic mechanisms. Here, we report two ultra-rare missense variants—AIPL1 R302L and BBS2 P134R—that co-segregate with early-onset nonsyndromic retinal degeneration in affected individuals from a non-consanguineous family. We performed a multi-level computational investigation to assess whether these variants may act through a convergent pathogenic mechanism. Using AlphaFold2-predicted structures, we modeled both wild-type and mutant proteins, introduced point mutations, and performed energy minimization and validation. FoldX, DynaMut2, and DUET all predicted destabilizing effects at the variant sites, corroborated by local disruption of secondary structure and altered surface electrostatics. Comparative docking (via HDOCK and ClusPro) identified a putative interaction interface between the TPR domain of AIPL1 and the β-sheet face of BBS2. This interface was destabilized in the double-mutant model. At the systems level, transcriptomic profiling confirmed co-expression of AIPL1 and BBS2 in human retina and fetal eye, while functional enrichment analysis highlighted overlapping involvement in ciliary and proteostasis pathways. Network propagation suggested that the two proteins may converge on shared interactors relevant to photoreceptor maintenance. Collectively, these in silico results provide structural and systems-level support for a candidate digenic mechanism involving AIPL1 and BBS2. While experimental validation remains necessary, our study proposes a testable mechanistic hypothesis and underscores the value of computational approaches in uncovering complex genetic contributions to IRDs. Full article

The allosteric activation of antithrombin (AT) involves a conformational shift from a native, repressed (R) to a heparin-bound, activated (AH) state. Using computational structural analysis, we identified an evolutionarily conserved allosteric communication network (ACN) comprising the residues H120, Y131, and Y166, which undergo key structural displacements during this transition. Site-directed mutagenesis of these residues markedly enhanced AT native reactivity toward FXa and reduced thermal stability, indicating their role in stabilizing the R state. These findings support a three-step “slingshot” model in which the ACN functions as a molecular lock that restrains stored conformational energy, preventing premature activation. Heparin binding disengages this lock, triggering a cascade of structural changes that propagate from the heparin-binding site (HBS) to the reactive center loop (RCL). Additional mutational analyses of residues bridging the β-sheet A (βsA) and the RCL/exosite domains revealed a delicate energetic balance involving the S380 insertion and E381–R197 salt bridge, which collectively tune the activation threshold. Molecular dynamics simulations of ACN mutants further revealed increased flexibility at both HBS and RCL domains, consistent with concerted allosteric coupling. Together, these results provide new mechanistic insights into the structural basis of AT activation and suggest avenues for engineering heparin-independent AT variants. Full article

Soil erosion is a critical ecological challenge in semi-arid regions of China, particularly in the Yellow River Basin, where Pisha sandstone slopes undergo rapid degradation. Rill erosion, driven by rainfall and overland flow, destabilizes slopes and accelerates ecosystem degradation. To address this, we developed a multi-view stereo observation system that integrates Structure-from-Motion (SFM) and multi-view stereo (MVS) for high-precision, dynamic monitoring of rill erosion. Laboratory rainfall simulations were conducted under four inflow rates (2–8 L/min), corresponding to rainfall intensities of 30–120 mm/h. The erosion process was divided into four phases: infiltration and particle rolling, splash and sheet erosion, incipient rill incision, and mature rill networks, with erosion concentrated in the middle and lower slope sections. The SFM-MVS system achieved planimetric and vertical errors of 3.1 mm and 3.7 mm, respectively, providing approximately 25% higher accuracy and nearly 50% faster processing compared with LiDAR and UAV photogrammetry. Infiltration stabilized at approximately 6.2 mm/h under low flows (2 L/min) but declined to less than 4 mm/h under high flows (≥6 L/min), leading to intensified rill incision and coarse-particle transport (up to 21.4% of sediment). These results demonstrate that the SFM-MVS system offers a scalable and non-invasive method for quantifying erosion dynamics, with direct implications for field monitoring, ecological restoration, and soil conservation planning. Full article

Accurately simulating fuses is challenging because the fuse behavior is affected by a variety of thermal and electrical factors. This paper presents a SPICE fuse model and its parameterization procedure. The model mimics the physical behavior of the time–current characteristic including the transition region. For the parameterization only, the time–current characteristic of the fuse, its resistance at room temperature and the melting temperature of the conducting material are needed. The novelty of this SPICE fuse model is the mathematical derivation of a physically based correction factor that considers the temperature dependence of the electrical fuse conductivity. The correction factor is applied to the inverted time–current characteristic. A third-order Foster thermal equivalent network is fitted to the adapted fuse characteristic using a least square algorithm. After a Foster–Cauer transformation, the thermal equivalent network is integrated into the SPICE model. Exemplary LTSpice is used to show and validate the model’s wiring diagram. Comparisons show a very good agreement with data sheet characteristics for a variety of fuse types and current ratings. In the adiabatic and transition region—i.e., at low tripping times—the maximum relative error between the data sheet characteristic and the simulated characteristic was consistently below 15% and thus within the production parameter spread. Full article

Turbidite lobe reservoirs represent critical deep-sea hydrocarbon targets, yet preferred seepage channels within them remain poorly characterized. This paper establishes a method for identifying internal preferred seepage channels in turbidite lobe reservoirs using data including seismic, core, thin section, logging, and production performance, combined with neural network technology. A neural network model for predicting reservoir productivity types can be obtained by taking the average logging data of reservoir intervals as input and the reservoir productivity types categorized by meter oil production index calculated by actual production data as the target. By applying the trained neural network model and inputting actual logging attribute model, the reservoir productivity types of single wells are obtained. Using the attribute model of natural gamma ray, acoustic, neutron, density, deep lateral, and shallow lateral logs, which are built by using the actual logging data and Sequential Gaussian Simulation, and supervising with the single well reservoir productivity type, the reservoir productivity type at any position in the reservoir can be predicted. It predicts their spatial distribution characteristics, reveals the genetic mechanism of preferred seepage channels, and discusses the significance of identifying preferred seepage channels for oilfield development. The results show that the reservoir productivity types in the study area can be divided into five categories with progressive improvement in productivity (A, B, C, D, and E) according to the increase in oil production index per meter, among which Type E reservoirs represent typical preferred seepage channels. The attribute model of reservoir productivity types indicates that, horizontally, types E and B are locally developed in the study area, while types D, C, and A are widely distributed. The preferred seepage channels can be divided into two types according to the shape: zonal (length to width > 2:1) and sheet-like (length to width ≤ 2:1). Vertically, types C, D, and E are relatively well-developed in layers III and IV, whereas types A and B are more common in layers I and II. The vertical combination patterns of preferred seepage channels reveal four types, including homogeneous, bottom-dominated, top-dominated, and interbedded patterns. The formation of preferred seepage channels is influenced by both sedimentary and diagenetic processes, and sedimentary is the most important controlling factors. The identification of preferred seepage channels in turbidite lobe reservoirs is of great significance for formulating development policies and tapping remaining oil. Full article

The giant squid (Dosidicus gigas) is an abundant marine species with high protein content, making it a promising resource for the food and biomaterial industries. This study aimed to investigate the effect of temperature (25–100 °C) on the structural changes in sarcoplasmic, myofibrillar, and stromal proteins isolated from squid mantle. Fourier-transform infrared spectroscopy (FT-IR) and circular dichroism (CD) were employed to monitor modifications in secondary structure, while field emission scanning electron microscopy (FE-SEM) was used to examine morphological characteristics. The FT-IR analysis revealed temperature-induced transitions in amide I, II, and A bands, indicating unfolding and aggregation processes, particularly in myofibrillar and stromal proteins. CD results confirmed a loss of α-helix content and an increase in β-sheet structures with rising temperature, especially above 60 °C, suggesting progressive denaturation. FE-SEM micrographs illustrated clear morphological differences: sarcoplasmic proteins displayed smooth, amorphous structures; myofibrillar proteins exhibited fibrous, porous networks; and stromal proteins presented dense and layered morphologies. These findings highlight the different thermal sensitivities and structural behaviors of squid muscle proteins and provide insight into their potential functional applications in thermally processed foods and bio-based materials. Full article

Despite its positive impact on household income, parental labour migration negatively affects left-behind children (LBC) and their caregivers. Studies in various settings have reported different impacts on LBC, but less is known about the consequences faced by their caregivers. This qualitative study used in-depth interviews to collect data from caregivers of LBC whose parents migrated for employment. Participants were recruited using the snowball sampling technique, starting with the distribution of study information sheets through village offices in Belu and Malacca districts, Indonesia. Data analysis was guided by a qualitative data analysis framework, which involved several steps, including familiarisation with the data or transcripts, identification of a thematic framework, indexing the data, charting the data, and mapping and interpreting the entire data. The findings showed that despite some benefits, including monthly remittances and positive feelings of living with and receiving support from LBC experienced by some caregivers, parental labour migration negatively impacted most caregivers due to their caregiving roles and responsibilities. These challenges included (i) mental and physical health issues, (ii) impacts on daily food and healthcare needs, and (iii) difficulties in their social lives and overall well-being. The findings underscore the need for comprehensive support systems and interventions to address these challenges and improve caregiver well-being. Such support systems should include access to mental and physical health services, financial assistance, employment opportunities, and social support networks. Future large-scale studies are recommended to explore the various impacts of parental migration and caregiving roles and responsibilities on caregivers of LBC, as the findings can better inform the development of policies and interventions to support them. Full article

This study proposed a pH-driven co-precipitation strategy to overcome the limitations of traditional physical blending in functional improvement of a dual-protein system. The results demonstrated that, in comparison with the soy-pea blended protein (SPBP), the soy-pea co-precipitated protein (SPCP) showed a decrease in α-helix and β-sheet content, accompanied by in an increase in random coil structure. SPCP exhibited decreased fluorescence intensity, smaller particle size (from 392.2 to 176.1 nm) with increased absolute zeta-potential values (from −13.7 to −19.7 mV), reduced surface hydrophobicity (from 21,987.3 to 9744.8), and increased content of disulfide bonds. Structural optimization of SPCP significantly bolstered intermolecular interactions between SPI and PPI. Molecular docking simulations also validated the presence of abundant hydrophobic interactions and hydrogen bonds within in the blend system. These modifications significantly enhanced the solubility of SPCP (especially SPCP8.0). The rheological analysis further revealed that the storage modulus (G′) and loss modulus (G″) of SPCP8.0 were both higher than those of SPBP, while its tan δ was lower than that of SPBP, indicating synergistic interactions between proteins. These interactions contributed to the formation of a more stable three-dimensional network structure, thereby conferring it with superior gel properties. These findings provide theoretical foundations for improving the functional properties of plant-based dual-protein and their applications in plant-based meat production. Full article

The main goal of this study is to create a computational solver that analyzes the effects of magnetohydrodynamics (MHD) on heat radiation in Cu–water-based Prandtl nanofluid flow using artificial neural networks. Copper nanoparticles are utilized to boost the water-based fluid’s thermal effect. This study primarily focuses on heat transfer over a horizontal sheet, exploring different scenarios by varying key factors such as the magnetic field and thermal radiation properties. The mathematical model is formulated using partial differential equations (PDEs), which are then transformed into a corresponding set of ordinary differential equations (ODEs) through appropriate similarity transformations. The bvp4c solver is then used to simulate the numerical behavior. The effects of relevant parameters on the temperature, velocity, skin friction, and local Nusselt number profiles are examined. It is discovered that the parameters of the Prandtl fluid have a considerable impact. The local skin friction and the local Nusselt number are improved by increasing these parameters. The dataset is split into 70% training, 15% validation, and 15% testing. The ANN model successfully predicts skin friction and Nusselt number profiles, showing good agreement with numerical simulations. This hybrid framework offers a robust predictive approach for heat management systems in industrial applications. This study provides important insights for researchers and engineers aiming to comprehend flow characteristics and their behavior and to develop accurate predictive models. Full article

The electric energy metering box serves as a crucial node in power grid operations, offering essential protection for key components in the distribution network, such as smart meters, data acquisition terminals, and circuit breakers, thereby ensuring their safe and reliable operation. However, the non-metallic housings of these boxes are susceptible to aging under environmental stress, which can result in diminished flame-retardant properties and an increased risk of fire. Currently, there is a lack of rapid and accurate methods for assessing the fire resistance of non-metallic metering box enclosures. In this study, laser-induced breakdown spectroscopy (LIBS), which enables fast, multi-element, and non-contact analysis, was utilized to develop an effective assessment approach. Thermal aging experiments were conducted to systematically investigate the degradation patterns and mechanisms underlying the reduced flame-retardant performance of sheet molding compound (SMC), a representative non-metallic material used in metering box enclosures. The results showed that the intensity ratio of aluminum ionic spectral lines is highly correlated with the flame-retardant grade, serving as an effective performance indicator. On this basis, a one-dimensional convolutional neural network (1D-CNN) model was developed utilizing LIBS data, which achieved over 92% prediction accuracy for different flame-retardant grades on the test set and demonstrated high recognition accuracy for previously unseen samples. This method offers significant potential for rapid, on-site evaluation of flame-retardant grades of non-metallic electric energy metering boxes, thereby supporting the safe and reliable operation of power systems. Full article

Drilling-induced damage in fiber-reinforced polymer composite materials was measured excavating four laminates, basalt (B₁₄), glass (G₁₄) and their two sandwich type hybrids (B₄G₆B₄, G₄B₆G₄), with 6 mm twist drills at 1520 revolutions per minute and 0.10 mm rev⁻¹ under dry running with an uncoated high-speed steel (HSS-R), grind-coated high-speed steel (HSS-G) or physical vapor deposition-coated (high-speed steel coated with Titanium Nitride (TiN) and Titanium Aluminum Nitride (TiAlN)) drill bits. The hybrid sheets were deliberately incorporated to clarify how alternating basalt–glass architectures redistribute interlaminar stresses during drilling, while the hard, low-friction TiN and TiAlN ceramic coatings enhance cutting performance by forming a heat-resistant tribological barrier that lowers tool–workpiece adhesion, reduces interface temperature, and thereby suppresses thrust-induced delamination. Replacement of an uncoated, grind-coated, high-speed-steel drill (HSS-G) with the latter coats lowered the mechanical and thermal loads substantially: mean thrust fell from 79–94 N to 24–30 N, and peak workpiece temperatures from 112 °C to 74 °C. Accordingly, entry/exit oversize fell from 2.5–4.7% to under 0.6% and, from the surface, the SEM image displayed clean fiber severance rather than pull-out and matrix smear. By analysis of variance (ANOVA), 92.7% of the variance of thrust and 86.6% of that of temperature could be accounted for by the drill-bit factor, thus confirming that the coatings overwhelm the laminate structure and hybrid stacking simply redistribute, but cannot overcome, the former influence. Regression models and an artificial neural network optimized via meta-heuristic optimization foretold thrust, temperature and delamination with an R² value of 0.94 or higher, providing an instant-screening device with which to explore industrial application. The work reveals TiAlN- and TiN-coated drills as financially competitive alternatives with which to achieve ±1% dimensional accuracy and minimum subsurface damage during multi-material composite machining. Full article

The kinetics of insulin aggregation and fibril formation were studied in vitro using Scanning Electron Microscopy (SEM) and Fourier Transform Infrared (FTIR) spectroscopy. Our investigation centered on the protein’s morphological and structural changes to better understand the transient molecular configurations that occur during the lag phase. SEM images showed that, already at early incubation stages, a network of disordered pseudo-filaments, ranging in length between 200 and 500 nanometers, develops on the surface of large aggregates. At later stages, fibrils catalyzed by protein aggregates were observed. Principal Component Analysis (PCA) of the FTIR data identified signatures of intramolecular β-sheet secondary structures forming during the lag phase and at the onset of the exponential growth phase. These absorption bands are linked to secondary nucleation mechanisms due to their transient nature. This interpretation is further supported by a chemical equilibrium model, which yielded a reliable secondary nucleation rate constant, K₂, on the order of 10⁴ M⁻² s⁻¹. Full article

Freshwater surimi typically exhibits poor gel-forming capability and is prone to gel deterioration, limiting its applications in food products. This study successfully prepared silver carp surimi gels with improved gel strength and water-holding capacity (WHC) using carboxymethyl konjac glucomannan (CKGM) as a functional modifier. Furthermore, the regulatory mechanism of CKGM with different degrees of substitution (DS) on the gel properties of silver carp surimi was systematically investigated. Results demonstrated that DS significantly influenced gel strength, WHC, and microstructure. CKGM (DS = 0.21%) substantially enhanced the gel strength and WHC through strengthened hydrophobic interactions and hydrogen-bond networks. However, CKGM with a higher DS (0.41%) induced a steric hindrance effect, decreasing elastic modulus and WHC and resulting in a more porous gel network. Raman spectroscopy analysis revealed that CKGM facilitated the conformational transition of myofibrillar proteins from α-helix to β-sheet, thereby improving the density of the gel network. The study provides theoretical foundations and technical guidance for the quality improvement of surimi products. Full article

This study investigated the gel performance of Spanish mackerel surimi gels (SMSGs) modified by three anionic polysaccharides: κ-carrageenan (KC), ι-carrageenan (IC), and gellan gum (GG). By incorporating polysaccharides, SMSGs showed a 24.9–103.4% improvement in gel and textural properties, in which KC and IC had more improvement effects than GG. Moreover, polysaccharides led to a 10.7–13.1% increment in WHC, a shortened water migration from 61.34 to 52.43–55.93 ms in T₂₂, and enhanced thermal stability of SMSGs. The content of α-helix in SMSGs reduced markedly accompanied by a concurrent enhancement of β-sheet and β-turn by adding polysaccharides, where β-sheet and β-turn are positively correlated with hardness being favorable for gelling. The microstructure of SMSGs/polysaccharides showed a homogeneous network mainly due to hydrophobic interactions and disulfide bonds in SMSG-based gels. This study will demonstrate the effectiveness of KC, IC, and GG in improving the texture and functionality as well as expanding the application of surimi products. Full article