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High-resolution and thermal infrared hyperspectral data acquired from the Thermal Infrared Airborne Spectrographic Imager (TASI) have been recognized as efficient tools in geology, demonstrating significant potential for rock discernment. Deep learning (DL), as an advanced technology, has driven substantial advancements in lithological mapping by automatically extracting high-level semantic features from images to enhance recognition accuracy. However, gathering sufficient high-quality lithological samples for model training is challenging in many scenarios, posing limitations for data-driven DL approaches. Moreover, existing sample collection approaches are plagued by limited verifiability, subjective bias, and variation in the spectra of the same class at different locations. To tackle these challenges, a novel sample generation method called multi-lithology spectra sample selection (MLS3) is first employed. This method involves multiple steps: multiple spectra extraction, spectra combination and optimization, lithological type identification, and sample selection. In this study, the TASI hyperspectral data collected from the Liuyuan area in Gansu Province, China, were used as experimental data. Samples generated based on MLS3 were fed into five typical DL models, including two-dimensional convolutional neural network (2D-CNN), hybrid spectral CNN (HybridSN), multiscale residual network (MSRN), spectral-spatial residual network (SSRN), and spectral partitioning residual network (SPRN) for lithological mapping. Among these models, the accuracy of the SPRN reaches 84.03%, outperforming the other algorithms. Furthermore, MLS3 demonstrates superior performance, achieving an overall accuracy of 2.25–6.96% higher than other sample collection methods when SPRN is used as the DL framework. In general, MLS3 enables both the quantity and quality of samples, providing inspiration for the application of DL to hyperspectral lithological mapping. [ABSTRACT FROM AUTHOR]

Coral reef bleaching events have become more frequent all over the world and pose a serious threat to coral reef ecosystems. Therefore, there is an urgent need for better detection of coral reef bleaching in a time- and cost-saving manner. In recent years, remote sensing technology has often been utilized and gained recognition for coral reef bleaching detection. However, bleaching corals in the water always have weak spectral change signals, causing difficulties in using remote sensing data. Additionally, uneven change samples make it challenging to adequately capture the details of coral reef bleaching detection and produce thematic maps. To resolve these problems, a novel method named coral reef bleaching detection by positive-unlabeled bagging (CBD-PUB) is proposed in this paper. To test the capacity of the method, a series of multi-temporal Sentinel-2 remote sensing images are utilized, and Lizard Island in Australia is taken as a case study area. The pseudo-invariant feature atmospheric correction (PIF) algorithm is adopted to improve coral reef bleaching spectral signals. After that, CBD-PUB is employed to effectively explore coral reef bleaching variation and its corresponding influence relations. The experimental results show that the overall accuracy of bleaching detection by the proposed algorithm reaches 92.1% and outperforms the traditional method. It fully demonstrates the feasibility of the model for the field of coral reef bleaching detection and provides assistance in the monitoring and protection of coral environments. [ABSTRACT FROM AUTHOR]

With the development of hyperspectral sensors, the availability of hyperspectral images (HSIs) has increased significantly, prompting advancements in deep learning-based hyperspectral image classification (HSIC) methods. Recently, graph convolutional networks (GCNs) have been proposed to process graph-structured data in non-Euclidean domains, and have been used for HSIC. The superpixel segmentation should be implemented first in the GCN-based methods, however, it is difficult to manually select the optimal superpixel segmentation sizes to obtain the useful information for classification. To solve this problem, we constructed a HSIC model based on a multiscale feature search-based graph convolutional network (MFSGCN) in this study. Firstly, pixel-level features of HSIs are extracted sequentially using 3D asymmetric decomposition convolution and 2D convolution. Then, superpixel-level features at different scales are extracted using multilayer GCNs. Finally, the neural architecture search (NAS) method is used to automatically assign different weights to different scales of superpixel features. Thus, a more discriminative feature map is obtained for classification. Compared with other GCN-based networks, the MFSGCN network can automatically capture features and obtain higher classification accuracy. The proposed MFSGCN model was implemented on three commonly used HSI datasets and compared to some state-of-the-art methods. The results confirm that MFSGCN effectively improves accuracy. [ABSTRACT FROM AUTHOR]

The molecular weight (MW) of an enzyme is a critical parameter in enzyme-constrained models (ecModels). It is determined by two factors: the presence of subunits and the abundance of each subunit. Although the number of subunits (NS) can potentially be obtained from UniProt, this information is not readily available for most proteins. In this study, we addressed this gap by extracting and curating subunit information from the UniProt database to establish a robust benchmark dataset. Subsequently, we propose a novel model named DeepSub, which leverages the protein language model and Bi-directional Gated Recurrent Unit (GRU), to predict NS in homo-oligomers solely based on protein sequences. DeepSub demonstrates remarkable accuracy, achieving an accuracy rate as high as 0.967, surpassing the performance of QUEEN. To validate the effectiveness of DeepSub, we performed predictions for protein homo-oligomers that have been reported in the literature but are not documented in the UniProt database. Examples include homoserine dehydrogenase from Corynebacterium glutamicum, Matrilin-4 from Mus musculus and Homo sapiens, and the Multimerins protein family from M. musculus and H. sapiens. The predicted results align closely with the reported findings in the literature, underscoring the reliability and utility of DeepSub. [ABSTRACT FROM AUTHOR]

Simple Summary: Heat stress (HS) constantly happens to dry lot feeding sheep during the summer, inevitably causing substantial losses to animal production due to its unfavorable effects on health, welfare and production performance. In the current study, HS played disadvantageous roles in growth performance, physiological indices, antioxidative capacity, immunomodulation, ruminal fermentation and microbial composition. Dietary supplementation with folic acid and taurine can effectively ameliorate adverse effects of HS by improving the proportion of beneficial bacteria such as Quinella and Succinivibrio and decreasing the abundance of harmful bacteria such as Asteroleplasma. This study provides a practical approach for the application of folic acid and taurine in HS-challenged sheep. The aim of this study was to investigate if the supplementation of folic acid and taurine can relieve the adverse effects of different levels of heat stress (HS) on growth performance, physiological indices, antioxidative capacity, immunity, rumen fermentation and microbiota. A total of 24 Dorper × Hu crossbred lambs (27.51 ± 0.96 kg) were divided into four groups: control group (C, 25 °C), moderate HS group (MHS, 35 °C), severe HS group (SHS, 40 °C), and the treatment group, under severe HS (RHS, 40 °C, 4 and 40 mg/kg BW/d coated folic acid and taurine, respectively). Results showed that, compared with Group C, HS significantly decreased the ADG of lambs (p < 0.05), and the ADG in the RHS group was markedly higher than in the MHS and SHS group (p < 0.05). HS had significant detrimental effects on physiological indices, antioxidative indices and immune status on the 4th day (p < 0.05). The physiological indices, such as RR and ST, increased significantly (p < 0.05) with the HS level and were significantly decreased in the RHS group, compared to the SHS group (p < 0.05). HS induced the significant increase of MDA, TNF-α, and IL-β, and the decrease of T-AOC, SOD, GPx, IL-10, IL-13, IgA, IgG, and IgM (p < 0.05). However, there was a significant improvement in these indices after the supplementation of folic acid and taurine under HS. Moreover, there were a significant increase in Quinella and Succinivibrio, and an evident decrease of the genera Rikenellaceae_RC9_gut_group and Asteroleplasma under HS (p < 0.05). The LEfSe analysis showed that the genera Butyrivibrio, Eubacterium_ventriosum_group, and f_Bifidobacteriaceae were enriched in the MHS, SHS and RHS groups, respectively. Correlated analysis indicated that the genus Rikenellaceae_RC9_gut_group was positively associated with MDA, while it was negatively involved in IL-10, IgA, IgM, and SOD (p < 0.05); The genus Anaeroplasma was positively associated with the propionate and valerate, while the genus Succinivibrio was negatively involved in TNF-α (p < 0.05). In conclusion, folic acid and taurine may alleviate the adverse effects of HS on antioxidant capacity, immunomodulation, and rumen fermentation of lambs by inducing changes in the microbiome that improve animal growth performance. [ABSTRACT FROM AUTHOR]

The interplay between the immune system and cancer underscores the central role of immunotherapy in cancer treatment. In this context, the innate immune system plays a critical role in preventing tumor invasion. Myeloid differentiation factor 88 (MyD88) is crucial for innate immunity, and activation of MyD88 promotes the production of inflammatory cytokines and induces infiltration, polarization, and immune escape of immune cells in the tumor microenvironment. Additionally, abnormal MyD88 signaling induces tumor cell proliferation and metastasis, which are closely associated with poor prognosis. Therefore, MyD88 could serve as a novel tumor biomarker and is a promising target for cancer therapy. Current strategies targeting MyD88 including inhibition of signaling pathways and protein multimerization, have made substantial progress, especially in inflammatory diseases and chronic inflammation-induced cancers. However, the specific role of MyD88 in regulating tumor immunity and tumorigenic mechanisms remains unclear. Therefore, this review describes the involvement of MyD88 in tumor immune escape and disease therapy. In addition, classical and non-classical MyD88 inhibitors were collated to provide insights into potential cancer treatment strategies. Despite several challenges and complexities, targeting MyD88 is a promising avenue for improving cancer treatment and has the potential to revolutionize patient outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: This research revealed that the downregulation of fumarate hydratase in patients is associated with poor prognosis. Mechanistically, FH binds to RAN, which inhibits the nuclear import of the PCSK9 transcription factor SREBF1/2, thus reducing the expression of PCSK9. This leads to increased clonal expansion of CD8+ T cells while the number of Tregs remains unchanged, and the expression of PD-L1 does not change significantly, thus enhancing the immunotherapy response. Importantly, combined therapy targeting PCSK9 and PD-1 may be beneficial for patients with CRC and low FH expression. Considering these results, our findings hold potential for future clinical applications. Despite the notable achievements of programmed death 1 (PD-1) antibodies in treating various cancers, the overall efficacy remains limited in the majority of colorectal cancer (CRC) cases. Metabolism reprogramming of tumors inhibits the tricarboxylic acid (TCA) cycle, leading to down-regulation of fumarate hydratase (FH), which is related to poor prognosis in CRC patients. By establishing a tumor-bearing mouse model of CRC with Fh1 expression deficiency, we confirmed that the therapeutic effect of PD-1 antibodies alone was suboptimal in mice with low Fh1 expression, which was improved by combination with a protein invertase subtilisin/kexin 9 (PCSK9) inhibitor. Mechanistically, FH binds to Ras-related nucleoprotein (RAN), which inhibits the nuclear import of the PCSK9 transcription factor SREBF1/2, thus reducing the expression of PCSK9. This leads to increased clonal expansion of CD8+ T cells while the number of Tregs remains unchanged, and the expression of PD-L1 does not change significantly, thus enhancing the immunotherapy response. On the contrary, the expression of PCSK9 increased in CRC cells with low FH expression, which antagonized the effects of immunotherapy. Overall, CRC patients with low FH expression may benefit from combinatorial therapy with PD-1 antibodies and PCSK9 inhibitors to enhance the curative effect. [ABSTRACT FROM AUTHOR]