Search Results (175)

Vitamin D insufficiency and deficiency are highly prevalent in patients with chronic kidney disease (CKD), and their pharmacokinetics are not well described. The primary study objective was to develop a population pharmacokinetic model of oral cholecalciferol (VitD₃) and its three major metabolites, 25-hydroxyvitamin D₃ (25D₃), 1,25-dihydroxyvitamin D₃ (1,25D₃), and 24,25-dihydroxyvitamin D₃ (24,25D₃), in CKD patients with vitamin D insufficiency and deficiency. CKD subjects (n = 29) were administered one dose of oral VitD₃ (5000 I.U.), and nonlinear mixed effects modeling was used to describe the pharmacokinetics of VitD₃ and its metabolites. The simultaneous fit of a two-compartment model for VitD₃ and a one-compartment model for each metabolite represented the observed data. A proportional error model explained the residual variability for each compound. No assessed covariate significantly affected the pharmacokinetics of VitD₃ and metabolites. Visual predictive plots demonstrated the adequate fit of the pharmacokinetic data of VitD₃ and metabolites. This is the first reported population pharmacokinetic modeling of VitD₃ and metabolites and has the potential to inform targeted dose individualization strategies for therapy in the CKD population. Based on the simulation, doses of 600 International Unit (I.U.)/day to 1000 I.U./day for 6 months are recommended to obtain the target 25D₃ concentration of between 30 and 60 ng/mL. These simulation findings could potentially contribute to the development of personalized dosage regimens for vitamin D treatment in patients with CKD. Full article

The structure and characteristics of LEOPs, β-oligosaccharides from the fruiting body of Lentinus edodes obtained via acid degradation and gel permeation chromatography, were investigated. We performed high-performance liquid chromatography, infrared spectroscopy, methylation analysis, nuclear magnetic resonance, and correlated activity experiments, including antioxidant, immunomodulatory, and liver injury protection to gain insights. LEOPs comprised an oligosaccharide (Mw 2445 Da) based on six β-1, 3-D-glucose residues as the main chain and six β-1, 6-D-glucose residues as the side chain. Surface plasmon resonance analysis indicated that LEOPs directly bound to dectin-1, which facilitated their immunoenhancing activity via downstream NF-κB activation. The results implied that LEOPs may be the active unit of the shiitake β-glucan. The determination of LEOPs structure was performed to reveal the anti-tumor effect and immune-regulatory function of shiitake β-glucan on a molecular level to provide a basis. Full article

Clustering algorithms have the characteristics of being simple and efficient and can complete calculations without a large number of datasets, making them suitable for application in noise reduction processing for audio module mass production testing. In order to solve the problems of the NMF algorithm easily getting stuck in local optimal solutions and difficult feature signal extraction, an improved NMF audio denoising algorithm combined with K-means initialization was designed. Firstly, the Euclidean distance formula of K-means has been improved to extract audio signal features from multiple dimensions. Combined with the initialization strategy of K-means decomposition, the initialization dictionary matrix of the NMF algorithm has been optimized to avoid getting stuck in local optimal solutions and effectively improve the robustness of the algorithm. Secondly, in the sparse coding part of the NMF algorithm, feature extraction expressions are added to solve the problem of noise residue and partial spectral signal loss in audio signals during the operation process. At the same time, the size of the coefficient matrix is limited to reduce operation time and improve the accuracy of feature extraction in high-precision audio signals. Then, comparative experiments were conducted using the NOIZEUS and NOISEX-92 datasets, as well as random noise audio signals. This algorithm improved the signal-to-noise ratio by 10–20 dB and reduced harmonic distortion by approximately −10 dB. Finally, a high-precision audio acquisition unit based on FPGA was designed, and practical applications have shown that it can effectively improve the signal-to-noise ratio of audio signals and reduce harmonic distortion. Full article

The formation of atroposelective biaryl compounds in plants and fungi is well understood; however, polyketide aglycone synthesis and dimerization in bacteria remain unclear. Thus, the biosynthetic gene cluster (BGC) responsible for antibacterial setomimycin production from Streptomyces nojiriensis JCM3382 was examined in comparison with the BGCs of spectomycin, julichromes, lincolnenins, and huanglongmycin. The setomimycin BGC includes post-polyketide synthase (PKS) assembly/cycling enzymes StmD (C-9 ketoreductase), StmE (aromatase), and StmF (thioesterase) as key components. The heterodimeric TcmI-like cyclases StmH and StmK are proposed to aid in forming the setomimycin monomer. In addition, StmI (P-450) is predicted to catalyze the biaryl coupling of two monomeric setomimycin units, with StmM (ferredoxin) specific to the setomimycin BGC. The roles of StmL and StmN, part of the nuclear transport factor 2 (NTF-2)-like protein family and unique to setomimycin BGCs, could particularly interest biochemists and combinatorial biologists. α-Glucosidase, a key enzyme in type 2 diabetes, hydrolyzes carbohydrates into glucose, thereby elevating blood glucose levels. This study aimed to assess the α-glucosidase inhibitory activity of EtOAc extracts of JCM 3382 and setomimycin. The JCM 3382 EtOAc extract and setomimycin exhibited greater potency than the standard inhibitor, acarbose, with IC₅₀ values of 285.14 ± 2.04 μg/mL and 231.26 ± 0.41 μM, respectively. Molecular docking demonstrated two hydrogen bonds with maltase-glucoamylase chain A residues Thr205 and Lys480 (binding energy = −6.8 kcal·mol⁻¹), two π–π interactions with Trp406 and Phe450, and one π–cation interaction with Asp542. Residue-energy analysis highlighted Trp406 and Phe450 as key in setomimycin’s binding to maltase-glucoamylase. These findings suggest that setomimycin is a promising candidate for further enzymological research and potential antidiabetic therapy. Full article

Traditional prosthetic liners are often limited in customization due to constraints in manufacturing processes and materials. Typically made from non-compressible elastomers, these liners can cause discomfort through uneven contact pressures and inadequate adaptation to the complex shape of the residual limb. This study explores the development of bioinspired cellular metamaterial prosthetic liners, designed using additive manufacturing techniques to improve comfort by reducing contact pressure and redistributing deformation at the limb–prosthesis interface. The gyroid unit cell was selected due to its favorable isotropic properties, ease of manufacturing, and ability to distribute loads efficiently. Following the initial unit cell identification analysis, the results from the uniaxial compression test on the metamaterial cellular samples were used to develop a multilinear material model, approximating the response of the metamaterial structure. Finite Element Analysis (FEA) using a previously developed generic limb–liner–socket model was employed to simulate and compare the biomechanical behavior of these novel liners against conventional silicone liners, focusing on key parameters such as peak contact pressure and liner deformation during donning, heel strike, and the push-off phase of the gait cycle. The results showed that while silicone liners provide good overall contact pressure reduction, cellular liners offer superior customization and performance optimization. The soft cellular liner significantly reduced peak contact pressure during donning compared to silicone liners but exhibited higher deformation, making it more suitable for sedentary individuals. In contrast, medium and hard cellular liners outperformed silicone liners for active individuals by reducing both contact pressure and deformation during dynamic gait phases, thereby enhancing stability. Specifically, a medium-density liner (10% infill) balanced contact pressure reduction with low deformation, offering a balance of comfort and stability. The hard cellular liner, ideal for high-impact activities, provided superior shape retention and support with lower liner deformation and comparable contact pressures to silicone liners. The results show that customizable stiffness in cellular metamaterial liners enables personalized design to address individual needs, whether focusing on comfort, stability, or both. These findings suggest that 3D-printed metamaterial liners could be a promising alternative to traditional prosthetic materials, warranting further research and clinical validation. Full article

An alkali-soluble β-glucan (AHEP-A-b, 20 kDa) purified from Hericium erinaceus fruiting bodies, was structurally characterized and examined for antioxidant activity. Methylation analysis and NMR spectroscopy show that the backbone of AHEP-A-b is composed of (1→6)-linked-D-β-glucopyran residues, branched at O-3 of glucopyranose (Glcp) residues with [→3)-β-D-Glcp-(1→] oligosaccharides or single unit of β-Glcp. Periodate oxidation analysis and matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) indicate that the degree of polymerization (DP) of [→3)-β-D-Glcp-(1→] side chains is 2 to 8. Functionally, AHEP-A-b is a relatively strong antioxidant as demonstrated by using 2, 2′-azino-bis-(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS) free radical (ABTS^·+), 1,1-diphenyl-2-picrylhydrazyl (DPPH) radicals, and hydroxyl radicals scavenging assays. The present study lays the foundation for further studies into structure-activity relationships of polysaccharides from H. erinaceus. Full article

Boric acid, H₃BO₃, is a molecular solid made up of layers held together by weak van der Waals forces. It can be considered a pseudo “2D” material, like graphite, compared to graphene. The key distinction is that within each individual layer, the molecular units are connected not only by strong covalent bonds but also by hydrogen bonds. Therefore, classic liquid exfoliation is not suitable for this material, and a specific method needs to be developed. Preliminary results of exfoliation of boric acid particles by combination of ultrasound and the use of surfactants are presented. Ultrasound provides the system with the energy needed for the process, and the surfactant can act to keep the crystalline flakes apart. A system consisting of a saturated solution and large excess solid residue of boric acid was treated in this way for a few hours at 40 °C in the presence of various sodium stearate, proving to be very promising, and an incipient exfoliation was achieved. Full article

Three new monosulfated polyhydroxysteroid glycosides, spiculiferosides A (1), B (2), and C (3), along with new related unsulfated monoglycoside, spiculiferoside D (4), were isolated from an ethanolic extract of the starfish Henricia leviuscula spiculifera collected in the Sea of Okhotsk. Compounds 1–3 contain two carbohydrate moieties, one of which is attached to C-3 of the steroid tetracyclic core, whereas another is located at C-24 of the side chain of aglycon. Two glycosides (2, 3) are biosides, and one glycoside (1), unlike them, includes three monosaccharide residues. Such type triosides are a rare group of polar steroids of sea stars. In addition, the 5-substituted 3-OSO₃-α-L-Araf unit was found in steroid glycosides from starfish for the first time. Cell viability analysis showed that 1–3 (at concentrations up to 100 μM) had negligible cytotoxicity against human embryonic kidney HEK293, melanoma SK-MEL-28, breast cancer MDA-MB-231, and colorectal carcinoma HCT 116 cells. These compounds significantly inhibited proliferation and colony formation in HCT 116 cells at non-toxic concentrations, with compound 3 having the greatest effect. Compound 3 exerted anti-proliferative effects on HCT 116 cells through the induction of dose-dependent cell cycle arrest at the G2/M phase, regulation of expression of cell cycle proteins CDK2, CDK4, cyclin D1, p21, and inhibition of phosphorylation of protein kinases c-Raf, MEK1/2, ERK1/2 of the MAPK/ERK1/2 pathway. Full article

This paper presents a 14-bit hybrid column-parallel compact analog-to-digital converter (ADC) for the application of digital infrared focal plane arrays (IRFPAs) with compromised power and speed performance. The proposed hybrid ADC works in two phases: in the first phase, a 7-bit successive approximation register (SAR) ADC performs coarse quantization; in the second phase, a 7-bit single-slope (SS) ADC performs fine quantization to complete the residue voltage conversion. In this work, the number of unit capacitors is reduced to 1/128th of that of a conventional 14-bit SAR ADC, which is beneficial for the application of small pixel-pitch IRFPAs. In this work, a tradeoff segmented thermometer-coded digital-to-analog converter (DAC) is adopted in the first 7-bit coarse quantization process: the lower 3-bit is binary coded, and the upper 4-bit is thermometer coded. A thermometer-coded DAC can improve the linearity of ADC. Capacitor array matching can be incredibly relaxed compared with a binary-weight 14-bit SAR ADC, resulting in a noncalibration feature. Moreover, by sharing DAC and comparator analog circuits between the SAR ADC and the SS ADC, the power consumption and layout area are consequently reduced. The proposed hybrid ADC was fabricated using a 180 nm CMOS process. The measurement results show that the proposed ADC has a differential nonlinearity of −0.61/+0.84 LSB and a sampling rate of 120 kS/s. The developed ADC achieves a temporal noise of 1.7 LSBrms at a temperature of 77 K. In addition, the SNDR is 72.9 dB, and the ENOB is 11.82 bit, respectively. Total power consumption is 71 μW from supply voltages of 3.3 V (analog) and 1.8 V (digital). Full article

Image dehazing aims to reconstruct potentially clear images from corresponding images corrupted by haze. With the rapid development of deep learning-related technologies, dehazing methods based on deep convolutional neural networks have gradually become mainstream. We note that existing dehazing methods often accompany an increase in computational overhead while improving the performance of dehazing. We propose a novel lightweight dehazing neural network to balance performance and efficiency: the g2D-Net. The g2D-Net borrows the design ideas of input-adaptive and long-range information interaction from Vision Transformers and introduces two kinds of convolutional blocks, i.e., the g2D Block and the FFT-g2D Block. Specifically, the g2D Block is a residual block with second-order gated units, which inherit the input-adaptive property of a gated unit and can realize the second-order interaction of spatial information. The FFT-g2D Block is a variant of the g2D Block, which efficiently extracts the global features of the feature maps through fast Fourier convolution and fuses them with local features. In addition, we employ the SK Fusion layer to improve the cascade fusion layer in a traditional U-Net, thus introducing the channel attention mechanism and dynamically fusing information from different paths. We conducted comparative experiments on five benchmark datasets, and the results demonstrate that the g2D-Net achieves impressive dehazing performance with relatively low complexity. Full article

The endo-β-1,4-xylanase glycosyl hydrolase (GH11) decomposes the backbone of xylan into xylooligosaccharides or xylose. These enzymes are important for industrial applications in the production of biofuel, feed, food, and value-added materials. β-D-xylopyranose (XYP, also known as β-D-xylose) is the fundamental unit of the substrate xylan, and understanding its recognition is fundamental for the initial steps of GH11’s molecular mechanism. However, little is known about the recognition of a single XYP molecule by GH11. In this study, the crystal structures of GH11 from Thermoanaerobacterium saccharolyticum (TsaGH11) complexed with an XYP molecule were determined at a resolution of 1.7–1.9 Å. The XYP molecule binds to subsite −2 of the substrate-binding cleft. The XYP molecule is mainly stabilized by a π–π interaction with the conserved Trp36 residue. The O2 and O3 atoms of XYP are stabilized by hydrogen bond interactions with the hydroxyl groups of Tyr96 and Tyr192. The conformation of the thumb domain of TsaGH11 does not play a critical role in XYP binding, and XYP binding induces a shift in the thumb domain of TsaGH11 toward the XYP molecule. A structural comparison of TsaGH11 with other GH11 xylanases revealed that the XYP molecule forms π–π stacking with the center between the phenyl and indoline ring of Trp36, whereas the XYP molecule unit from xylobiose or xylotetraose forms π–π stacking with the indoline of Trp36, which indicates that the binding modes of the substrate and XYP differ. These structural results provide a greater understanding of the recognition of XYP by the GH11 family. Full article

Dehazing individual remote sensing (RS) images is an effective approach to enhance the quality of hazy remote sensing imagery. However, current dehazing methods exhibit substantial systemic and computational complexity. Such complexity not only hampers the straightforward analysis and comparison of these methods but also undermines their practical effectiveness on actual data, attributed to the overtraining and overfitting of model parameters. To mitigate these issues, we introduce a novel dehazing network for non-uniformly hazy RS images: GLUENet, designed for both lightweightness and computational efficiency. Our approach commences with the implementation of the classical U-Net, integrated with both local and global residuals, establishing a robust base for the extraction of multi-scale information. Subsequently, we construct basic convolutional blocks using gated linear units and efficient channel attention, incorporating depth-separable convolutional layers to efficiently aggregate spatial information and transform features. Additionally, we introduce a fusion block based on efficient channel attention, facilitating the fusion of information from different stages in both encoding and decoding to enhance the recovery of texture details. GLUENet’s efficacy was evaluated using both synthetic and real remote sensing dehazing datasets, providing a comprehensive assessment of its performance. The experimental results demonstrate that GLUENet’s performance is on par with state-of-the-art (SOTA) methods and surpasses the SOTA methods on our proposed real remote sensing dataset. Our method on the real remote sensing dehazing dataset has an improvement of 0.31 dB for the PSNR metric and 0.13 for the SSIM metric, and the number of parameters and computations of the model are much lower than the optimal method. Full article

Building change detection (BCD) plays a vital role in city planning and development, ensuring the timely detection of urban changes near metro lines. Synthetic Aperture Radar (SAR) has the advantage of providing continuous image time series with all-weather and all-time capabilities for earth observation compared with optical remote sensors. Deep learning algorithms have extensively been applied for BCD to realize the automatic detection of building changes. However, existing deep learning-based BCD methods with SAR images suffer limited accuracy due to the speckle noise effect and insufficient feature extraction. In this paper, an attention-guided dual-branch fusion network (ADF-Net) is proposed for urban BCD to address this limitation. Specifically, high-resolution SAR images collected by TerraSAR-X have been utilized to detect building changes near metro line 8 in Shanghai with the ADF-Net model. In particular, a dual-branch structure is employed in ADF-Net to extract heterogeneous features from radiometrically calibrated TerraSAR-X images and log ratio images (i.e., difference images (DIs) in dB scale). In addition, the attention-guided cross-layer addition (ACLA) blocks are used to precisely locate the features of changed areas with the transformer-based attention mechanism, and the global attention mechanism with the residual unit (GAM-RU) blocks is introduced to enhance the representation learning capabilities and solve the problems of gradient fading. The effectiveness of ADF-Net is verified using evaluation metrics. The results demonstrate that ADF-Net generates better building change maps than other methods, including U-Net, FC-EF, SNUNet-CD, A2Net, DMINet, USFFCNet, EATDer, and DRPNet. As a result, some building area changes near metro line 8 in Shanghai have been accurately detected by ADF-Net. Furthermore, the prediction results are consistent with the changes derived from high-resolution optical remote sensing images. Full article

In this paper, we propose a dense multi-scale adaptive graph convolutional network (DMA-GCN) method for automatic segmentation of the knee joint cartilage from MR images. Under the multi-atlas setting, the suggested approach exhibits several novelties, as described in the following. First, our models integrate both local-level and global-level learning simultaneously. The local learning task aggregates spatial contextual information from aligned spatial neighborhoods of nodes, at multiple scales, while global learning explores pairwise affinities between nodes, located globally at different positions in the image. We propose two different structures of building models, whereby the local and global convolutional units are combined by following an alternating or a sequential manner. Secondly, based on the previous models, we develop the DMA-GCN network, by utilizing a densely connected architecture with residual skip connections. This is a deeper GCN structure, expanded over different block layers, thus being capable of providing more expressive node feature representations. Third, all units pertaining to the overall network are equipped with their individual adaptive graph learning mechanism, which allows the graph structures to be automatically learned during training. The proposed cartilage segmentation method is evaluated on the entire publicly available Osteoarthritis Initiative (OAI) cohort. To this end, we have devised a thorough experimental setup, with the goal of investigating the effect of several factors of our approach on the classification rates. Furthermore, we present exhaustive comparative results, considering traditional existing methods, six deep learning segmentation methods, and seven graph-based convolution methods, including the currently most representative models from this field. The obtained results demonstrate that the DMA-GCN outperforms all competing methods across all evaluation measures, providing $DSC=95.71\%$ and $DSC=94.02\%$ for the segmentation of femoral and tibial cartilage, respectively. Full article

The planning of interventions aimed at preserving the built heritage of inner areas is a complex process due to the fragility of these contexts. It should stem from adequate knowledge to support the recognition of qualities, resources, and potentials, and the reinterpretation of residual values. From the perspective of an axiological approach to the built heritage, it is possible to foster the resemantization of such values based on a rigorous cognitive model. This research proposed a cognitive model of the built heritage of the historic neighbourhood of Granfonte in Leonforte (Enna). The knowledge model, developed in Excel, has a hierarchical type of structure characterized by domain, classes, properties, and the attribution of values to properties. The approach makes it possible to execute queries that arise from specific relationships between classes. In this study, we developed both simple queries to measure the percentages of certain characteristics of the building units and complex queries for the estimation of aggregate indices to define the degree of transformation and loss of integrity $I_{\mathrm{T}\mathrm{I}}$ and degradation $I_{\mathrm{D}}$ of the building units or to identify those most exposed to the risk of ruination and contagion. The proposed model can be framed within the framework of ontologies supporting structured heritage knowledge. Full article