Multi-omic analysis of Huntington's disease reveals a compensatory astrocyte state.

The mechanisms underlying the selective regional vulnerability to neurodegeneration in Huntington's disease (HD) have not been fully defined. To explore the role of astrocytes in this phenomenon, we used single-nucleus and bulk RNAseq, lipidomics, HTT gene CAG repeat-length measurements, and multiplexed immunofluorescence on HD and control post-mortem brains. We identified genes that correlated with CAG repeat length, which were enriched in astrocyte genes, and lipidomic signatures that implicated poly-unsaturated fatty acids in sensitizing neurons to cell death. Because astrocytes play essential roles in lipid metabolism, we explored the heterogeneity of astrocytic states in both protoplasmic and fibrous-like (CD44+) astrocytes. Significantly, one protoplasmic astrocyte state showed high levels of metallothioneins and was correlated with the selective vulnerability of distinct striatal neuronal populations. When modeled in vitro, this state improved the viability of HD-patient-derived spiny projection neurons. Our findings uncover key roles of astrocytic states in protecting against neurodegeneration in HD.

Bio-based sunflower carbon/polyethylene glycol shape-stabilized phase change materials for thermal energy storage.

The exploitation of shape-stabilized phase change materials with high thermal conductivity and energy storage capacity is an effective strategy for improving energy efficiency. In this work, sunflower stem carbon/polyethylene glycol (SS-PEG) and sunflower receptacle carbon/polyethylene glycol (SR-PEG) shape-stabilized phase change materials, utilizing sunflower stem and receptacle biomass carbon with high specific surface area and pore volume obtained by carbonization as frameworks and polyethylene glycol as an energy storage material, were prepared by the vacuum impregnation method. The ability to load polyethylene glycol into the pore structure of carbon materials in different sunflower parts was mainly investigated, and the micro-morphology, compositional structure and thermal properties were characterized and analyzed using SEM, IR spectroscopy, XRD, DSC and TG techniques. The results showed that the carbonized sunflower stems maintained the sieve pore structure, and the carbonized sunflower receptacle was a macroporous structure containing a large number of three-dimensional interconnections. At the same time, the interaction between polyethylene glycol and each carbon material occurred through physisorption. The melting enthalpies of SS-PEG and SR-PEG shape-stabilized phase change materials were 153.4 J g-1 and 171.5 J g-1, respectively, and the loading rates reached 81.9% and 91.5%, with initial thermal decomposition temperatures (T 5%) of 344 °C and 368 °C.

Investigating the Corrosion Resistance of Different SiC Crystal Types: From Energy Sectors to Advanced Applications.

Silicon carbide, as a third-generation semiconductor material, plays a pivotal role in various advanced technological applications. Its exceptional stability under extreme conditions has garnered a significant amount of attention. These superior characteristics make silicon carbide an ideal candidate material for high-frequency, high-power electronic devices and applications in harsh environments. In particular, corrosion resistance in natural or artificially acidic and alkaline environments limits the practical application of many other materials. In fields such as chemical engineering, energy conversion, and environmental engineering, materials often face severe chemical erosion, necessitating materials with excellent chemical stability as foundational materials, carriers, or reaction media. Silicon carbide exhibits outstanding performance under these conditions, demonstrating significant resistance to corrosive substances such as hydrochloric acid, sulfuric acid, nitric acid, and alkaline substances such as potassium hydroxide and sodium hydroxide. Despite the well-known chemical stability of silicon carbide, the stability conditions of its different types (such as 3C-, 4H-, and 6H-SiC polycrystals) in acidic and alkaline environments, as well as the specific corrosion mechanisms and differences, warrant further investigation. This Review not only delves deeply into the detailed studies related to this topic but also highlights the current applications of different silicon carbide polycrystals in chemical reaction systems, energy conversion equipment, and recycling processes. Through a comprehensive analysis, this Review aims to bridge research gaps, offering a comparative analysis of the advantages and disadvantages between different polymorphs. It provides material scientists, engineers, and developers with a thorough understanding of silicon carbide's behavior in various chemical environments. This work will propel the research and development of silicon carbide materials under extreme conditions, especially in areas where chemical stability is crucial for device performance and durability. It lays a solid foundation for ultra-high-power, high-integration, high-reliability module architectures, supercomputing chips, and highly safe long-life batteries.

EWT: Efficient Wavelet-Transformer for single image denoising.

Transformer-based image denoising methods have shown remarkable potential but suffer from high computational cost and large memory footprint due to their linear operations for capturing long-range dependencies. In this work, we aim to develop a more resource-efficient Transformer-based image denoising method that maintains high performance. To this end, we propose an Efficient Wavelet Transformer (EWT), which incorporates a Frequency-domain Conversion Pipeline (FCP) to reduce image resolution without losing critical features, and a Multi-level Feature Aggregation Module (MFAM) with a Dual-stream Feature Extraction Block (DFEB) to harness hierarchical features effectively. EWT achieves a faster processing speed by over 80% and reduces GPU memory usage by more than 60% compared to the original Transformer, while still delivering denoising performance on par with state-of-the-art methods. Extensive experiments show that EWT significantly improves the efficiency of Transformer-based image denoising, providing a more balanced approach between performance and resource consumption.

Multimodal Co-attention Fusion Network with Online Data Augmentation for Cancer Subtype Classification.

It is an essential task to accurately diagnose cancer subtypes in computational pathology for personalized cancer treatment. Recent studies have indicated that the combination of multimodal data, such as whole slide images (WSIs) and multi-omics data, could achieve more accurate diagnosis. However, robust cancer diagnosis remains challenging due to the heterogeneity among multimodal data, as well as the performance degradation caused by insufficient multimodal patient data. In this work, we propose a novel multimodal co-attention fusion network (MCFN) with online data augmentation (ODA) for cancer subtype classification. Specifically, a multimodal mutual-guided co-attention (MMC) module is proposed to effectively perform dense multimodal interactions. It enables multimodal data to mutually guide and calibrate each other during the integration process to alleviate inter- and intra-modal heterogeneities. Subsequently, a self-normalizing network (SNN)-Mixer is developed to allow information communication among different omics data and alleviate the high-dimensional small-sample size problem in multi-omics data. Most importantly, to compensate for insufficient multimodal samples for model training, we propose an ODA module in MCFN. The ODA module leverages the multimodal knowledge to guide the data augmentations of WSIs and maximize the data diversity during model training. Extensive experiments are conducted on the public TCGA dataset. The experimental results demonstrate that the proposed MCFN outperforms all the compared algorithms, suggesting its effectiveness.

Cost-Sensitive Weighted Contrastive Learning Based on Graph Convolutional Networks for Imbalanced Alzheimer's Disease Staging.

Identifying the progression stages of Alzheimer's disease (AD) can be considered as an imbalanced multi-class classification problem in machine learning. It is challenging due to the class imbalance issue and the heterogeneity of the disease. Recently, graph convolutional networks (GCNs) have been successfully applied in AD classification. However, these works did not handle the class imbalance issue in classification. Besides, they ignore the heterogeneity of the disease. To this end, we propose a novel cost-sensitive weighted contrastive learning method based on graph convolutional networks (CSWCL-GCNs) for imbalanced AD staging using resting-state functional magnetic resonance imaging (rs-fMRI). The proposed method is developed on a multi-view graph constructed using the functional connectivity (FC) and high-order functional connectivity (HOFC) features of the subjects. A novel cost-sensitive weighted contrastive learning procedure is proposed to capture discriminative information from the minority classes, encouraging the samples in the minority class to provide adequate supervision. Considering the heterogeneity of the disease, the weights of the negative pairs are introduced into contrastive learning and they are computed based on the distance to class prototypes, which are automatically learned from the training data. Meanwhile, the cost-sensitive mechanism is further introduced into contrastive learning to handle the class imbalance issue. The proposed CSWCL-GCN is evaluated on 720 subjects (including 184 NCs, 40 SMC patients, 208 EMCI patients, 172 LMCI patients and 116 AD patients) from the ADNI (Alzheimer's Disease Neuroimaging Initiative). Experimental results show that the proposed CSWCL-GCN outperforms state-of-the-art methods on the ADNI database.

Expression, purification, and crystal structure of mpox virus A41 protein.

Mpox is a zoonotic disease that was once endemic in Africa countries caused by mpox virus. However, cases recently have been confirmed in many non-endemic countries outside of Africa. The rapidly increasing number of confirmed mpox cases poses a threat to the international community. In-depth studies of key viral factors are urgently needed, which will inform the design of multiple antiviral agents. Mpox virus A41L gene encodes a secreted protein, A41, that is nonessential for viral replication, but could affect the host response to infection via interacting with chemokines. Here, mpox virus A41 protein was expressed in Sf9 cells, and purified by affinity chromatography followed by gel filtration. Surface plasmon resonance spectroscopy showed that purified A41 binds a certain human chemokine CXCL8 with the equilibrium dissociation constant (KD) being 1.22 × 10-6 M. The crystal structure of mpox virus A41 protein was solved at 1.92 Å. Structural analysis and comparison revealed that mpox virus A41 protein adopts a characteristic ß-sheet topology, showing minor differences with that of vaccinia virus. These preliminary structural and functional studies of A41 protein from mpox virus will help us better understand its role in chemokine subversion, and contributing to the knowledge to viral chemokine binding proteins.

Involution Transformer Based U-Net for Landmark Detection in Ultrasound Images for Diagnosis of Infantile DDH.

The B-mode ultrasound based computer-aided diagnosis (CAD) has demonstrated its effectiveness for diagnosis of Developmental Dysplasia of the Hip (DDH) in infants, which can conduct the Graf's method by detecting landmarks in hip ultrasound images. However, it is still necessary to explore more valuable information around these landmarks to enhance feature representation for improving detection performance in the detection model. To this end, a novel Involution Transformer based U-Net (IT-UNet) network is proposed for hip landmark detection. The IT-UNet integrates the efficient involution operation into Transformer to develop an Involution Transformer module (ITM), which consists of an involution attention block and a squeeze-and-excitation involution block. The ITM can capture both the spatial-related information and long-range dependencies from hip ultrasound images to effectively improve feature representation. Moreover, an Involution Downsampling block (IDB) is developed to alleviate the issue of feature loss in the encoder modules, which combines involution and convolution for the purpose of downsampling. The experimental results on two DDH ultrasound datasets indicate that the proposed IT-UNet achieves the best landmark detection performance, indicating its potential applications.

Weakly Supervised Lesion Detection and Diagnosis for Breast Cancers With Partially Annotated Ultrasound Images.

Deep learning (DL) has proven highly effective for ultrasound-based computer-aided diagnosis (CAD) of breast cancers. In an automatic CAD system, lesion detection is critical for the following diagnosis. However, existing DL-based methods generally require voluminous manually-annotated region of interest (ROI) labels and class labels to train both the lesion detection and diagnosis models. In clinical practice, the ROI labels, i.e. ground truths, may not always be optimal for the classification task due to individual experience of sonologists, resulting in the issue of coarse annotation to limit the diagnosis performance of a CAD model. To address this issue, a novel Two-Stage Detection and Diagnosis Network (TSDDNet) is proposed based on weakly supervised learning to improve diagnostic accuracy of the ultrasound-based CAD for breast cancers. In particular, all the initial ROI-level labels are considered as coarse annotations before model training. In the first training stage, a candidate selection mechanism is then designed to refine manual ROIs in the fully annotated images and generate accurate pseudo-ROIs for the partially annotated images under the guidance of class labels. The training set is updated with more accurate ROI labels for the second training stage. A fusion network is developed to integrate detection network and classification network into a unified end-to-end framework as the final CAD model in the second training stage. A self-distillation strategy is designed on this model for joint optimization to further improves its diagnosis performance. The proposed TSDDNet is evaluated on three B-mode ultrasound datasets, and the experimental results indicate that it achieves the best performance on both lesion detection and diagnosis tasks, suggesting promising application potential.

The synergistic anticancer effect of CBD and DOX in osteosarcoma

Background Osteosarcoma is a malignant tumor that can present with pain in the bones, joints, and local masses. The incidence is highest in adolescents, and the most common sites are the distal femur, proximal tibia and proximal humerus metaphyseal. Doxorubicin is the first-line chemotherapeutic agent for the treatment of osteosarcoma, but it has many side effects. Cannabidiol is a non-psychoactive plant cannabinoid cannabinol (CBD) that has been shown to be effective against osteosarcoma; however, the molecular targets and mechanisms of CBD action in osteosarcoma remain unclear. Methods Cell proliferation, migration, invasion and colony formation were analyzed using two drugs alone or in combination to evaluate their inhibitory effects on the malignant characteristics of OS cells. Apoptosis and the cell cycle were detected by flow cytometry. The synergistic inhibitory effect of doxorubicin/cannabidiol on tumors was also detected in nude mouse xenotransplantation models. Results Through analysis of two osteosarcoma cell lines, MG63 and U2R, it was found that the cannabidiol/doxorubicin combination treatment synergistically inhibited growth, migration and invasion and induced apoptosis, blocking G2 stagnation in OS cells. Further mechanistic exploration suggests that the PI3K-AKT-mTOR pathway and MAPK pathway play an important role in the synergistic inhibitory effect of the two drugs in osteosarcoma. Finally, in vivo experimental results showed that the cannabidiol/doxorubicin combination treatment significantly reduced the number of tumor xenografts compared to cannabidiol alone or doxorubicin alone. Conclusions Our findings in this study suggest that cannabidiol and doxorubicin have a synergistic anticancer effect on OS cells, and their combined application may be a promising treatment strategy for OS (AU)

Post-Thaw Storage Temperature Influenced Boar Sperm Quality and Lifespan through Apoptosis and Lipid Peroxidation.

Cryopreservation deteriorates boar sperm quality and lifespan, which restricts the use of artificial insemination with frozen-thawed boar semen in field conditions. The objective of this study was to test the effects of post-thaw storage time and temperature on boar sperm survival. Semen ejaculates from five Landrace boars (one ejaculate per boar) were collected and frozen following a 0.5 mL-straw protocol. Straws from the five boars were thawed and diluted 1:1 (v:v) in BTS. The frozen-thawed semen samples were aliquoted into three parts and respectively stored at 5 °C, 17 °C, and 37 °C for up to 6 h. At 0.5, 2, and 6 h of storage, sperm motility, viability, mitochondrial membrane potential, and intracellular reactive oxygen species (ROS) levels and apoptotic changes were measured. Antioxidant and oxidant levels were tested in boar sperm (SPZ) and their surrounding environment (SN) at each timepoint. The results showed significant effects of post-thaw storage time and temperature and an impact on boar sperm quality (total and progressive motility, VCL, viability, acrosome integrity), early and late sperm apoptotic changes, and changes in MDA levels in SPZ and SN. Compared to storage at 5 °C and 37 °C, frozen-thawed semen samples stored at 17 °C displayed better sperm quality, less apoptotic levels, and lower levels of SPZ MDA and SN MDA. Notably, post-thaw storage at 17 °C extended boar sperm lifespan up to 6 h without obvious reduction in sperm quality. In conclusion, storage of frozen-thawed boar semen at 17 °C preserves sperm quality for up to 6 h, which facilitates the use of cryopreserved boar semen for field artificial insemination.