Platelet-targeted gene therapy with human factor VIII establishes haemostasis in dogs with haemophilia A

It is essential to improve therapies for controlling excessive bleeding in patients with haemorrhagic disorders. As activated blood platelets mediate the primary response to vascular injury, we hypothesize that storage of coagulation Factor VIII within platelets may provide a locally inducible treatment to maintain haemostasis for haemophilia A. Here we show that haematopoietic stem cell gene therapy can prevent the occurrence of severe bleeding episodes in dogs with haemophilia A for at least 2.5 years after transplantation. We employ a clinically relevant strategy based on a lentiviral vector encoding the ITGA2B gene promoter, which drives platelet-specific expression of human FVIII permitting storage and release of FVIII from activated platelets. One animal receives a hybrid molecule of FVIII fused to the von Willebrand Factor propeptide-D2 domain that traffics FVIII more effectively into α-granules. The absence of inhibitory antibodies to platelet-derived FVIII indicates that this approach may have benefit in patients who reject FVIII replacement therapies. Thus, platelet FVIII may provide effective long-term control of bleeding in patients with haemophilia A. Haemophilia is a genetic bleeding disorder associated with a deficiency in the coagulation factor VIII. Here, the authors use gene therapy to achieve stable overexpression of factor VIII in platelets of dogs with haemophilia A, preventing the occurrence of severe bleeding episodes for over 2.5 years

Blank Strip Filling for Logging Electrical Imaging Based on Multiscale Generative Adversarial Network

The Fullbore Formation Micro Imager (FMI) represents a proficient method for examining subterranean oil and gas deposits. Despite its effectiveness, due to the inherent configuration of the borehole and the logging apparatus, the micro-resistivity imaging tool cannot achieve complete coverage. This limitation manifests as blank regions on the resulting micro-resistivity logging images, thus posing a challenge to obtaining a comprehensive analysis. In order to ensure the accuracy of subsequent interpretation, it is necessary to fill these blank strips. Traditional inpainting methods can only capture surface features of an image, and can only repair simple structures effectively. However, they often fail to produce satisfactory results when it comes to filling in complex images, such as carbonate formations. In order to address the aforementioned issues, we propose a multiscale generative adversarial network-based image inpainting method using U-Net. Firstly, in order to better fill the local texture details of complex well logging images, two discriminators (global and local) are introduced to ensure the global and local consistency of the image; the local discriminator can better focus on the texture features of the image to provide better texture details. Secondly, in response to the problem of feature loss caused by max pooling in U-Net during down-sampling, the convolution, with a stride of two, is used to reduce dimensionality while also enhancing the descriptive ability of the network. Dilated convolution is also used to replace ordinary convolution, and multiscale contextual information is captured by setting different dilation rates. Finally, we introduce residual blocks on the U-Net network in order to address the degradation problem caused by the increase in network depth, thus improving the quality of the filled logging images. The experiment demonstrates that, in contrast to the majority of existing filling algorithms, the proposed method attains superior outcomes when dealing with the images of intricate lithology

Fault Identification of U-Net Based on Enhanced Feature Fusion and Attention Mechanism

Accurate fault identification is essential for geological interpretation and reservoir exploitation. However, the unclear and noisy composition of seismic data makes it difficult to identify the complete fault structure using conventional methods. Thus, we have developed an attentional U-shaped network (EAResU-net) based on enhanced feature fusion for automated end-to-end fault interpretation of 3D seismic data. EAResU-net uses an enhanced feature fusion mechanism to reduce the semantic gap between the encoder and decoder and improve the representation of fault features in combination with residual structures. In addition, EAResU-net introduces an attention mechanism, which effectively suppresses seismic data noise and improves model accuracy. The experimental results on synthetic and field data demonstrate that, compared with traditional deep learning methods for fault detection, our EAResU-net can achieve more accurate and continuous fault recognition results

Enhanced Antifungal Activities of Eugenol-Entrapped Casein Nanoparticles against Anthracnose in Postharvest Fruits

This study aims to improve the antifungal effects of eugenol through low-energy self-assembly fabrication and optimization of eugenol-casein nanoparticles (EC-NPs). Optimized EC-NPs (eugenol/casein ratio of 1:5) were obtained with a mean size of 307.4 ± 2.5 nm and entrapment efficiency of 86.3% ± 0.2%, and showed high stability under incubated at 20 and 37 °C for 48 h. EC-NPs exhibited satisfactory sustained-release effect at 20 °C or 37 °C, with remaining eugenols amounts of 79.51% and 53.41% after 72 h incubation, respectively, which were significantly higher than that of native eugenol (only 26.40% and 19.82% after the first 12 h). EC-NPs exhibited a greater antifungal activity (>95.7%) against spore germination of fungus that was greater than that of native eugenol, showed 100% inhibition of the anthracnose incidence in postharvest pear after 7 d. EC-NPs is potential as an environmental-friendly preservatives in the food industry

A staggered-grid lowrank finite-difference method for elastic wave extrapolation

Elastic wave extrapolation in the time domain is significant for an elastic wave equation-based processing. To improve the simulation reliability and accuracy of decoupled elastic P- and S- waves, we propose the staggered-grid lowrank finite-difference method based on the elastic wave decomposition. For elastic wave propagation, a lowrank finite-difference method based on the staggered grid is derived to improve the accuracy. Regarding the application of the decoupled elastic wave equation, we derive the finite-difference scheme coefficients which are dependent on velocity. Based on the elastic wave decomposition and plane wave theories, we formulate the elastic wave-extrapolation operators, which contain trigonometric adjustment factors. Accordingly, by applying the lowrank method to approximating the operators, the finite- difference scheme is designed to discretize the decoupled wave equation. The derivation processing implies the combination of elastic wave-mode decomposition and extrapolation. The proposed method enables elastic P- and S-waves to extrapolate in the time-space domain separately and produces accurate P-and S-wave components simultaneously. Dispersion analysis suggests that our proposed method is reliable and accurate in a wide range of wavenumber. Numerical simulation tests on a simple model and the Marmousi2 model validate the accuracy and effectiveness of the method, showing its ability in handling complex structures. Although the operators are accurate only when the medium is homogeneous, they are of high accuracy when the velocity gradient is quite small and are applicable when the velocity gradient is large. The subsequent results of reverse time migration for the Marmousi2 model also suggest that the proposed method is enough to serve as an extrapolator in elastic reverse time migration

Adsorption/Desorption on Macroporous Resins of Okicamelliaside in the Extract of <i>Camellia nitidissima</i> Chi Leaves

Okicamelliaside (OCS) from Camellia nitidissima Chi (C. nitidissima) leaves can be used in therapeutic drugs or nutritional foods. However, which resin is the best for separating OCS and the underlying mechanism for its superiority remains unclear. In this study, the differences in the adsorption/desorption effectiveness and adsorption kinetics of OCS on five resins were compared. AB-8 was found to be an effective resin for the separation of OCS and the adsorption kinetics followed a pseudo-first order model (R2 > 0.99). In order to optimize the separation of OCS by the resin AB-8, the adsorption time, OCS sample concentration, eluent solvent and volume were tested using a 7 mL column with a diameter of 2 cm. The results showed that the optimum adsorption time was 30 min and the optimum sample concentration was 2.5 mg/mL, while the optimum desorption was achieved by using 2.1 times column volume of 60% ethanol solution. The separation yielded a purified extract with OCS of 290.82 (±2.17) mg/g, which was 6.0 times more than the crude extract (E1, 48.51 (±0.56) mg/g of OCS). This study highlights the use of AB-8 resin for the separation of OCS as an effective technique on the basis of the adsorption/desorption of OCS on the resin. The method has the potential for obtaining green OCS extract with a high OCS content from the crude extract of the leaves of C. nitidissima