Cotton transposon-related variome reveals roles of transposon-related variations in modern cotton cultivation.

INTRODUCTION: Transposon plays a vital role in cotton genome evolution, contributing to the expansion and divergence of genomes within the Gossypium genus. However, knowledge of transposon activity in modern cotton cultivation is limited. OBJECTIVES: In this study, we aimed to construct transposon-related variome within Gossypium genus and reveal role of transposon-related variations during cotton cultivation. In addition, we try to identify valuable transposon-related variations for cotton breeding. METHODS: We utilized graphical genome construction to build up the graphical transposon-related variome. Based on the graphical variome, we integrated t-test, eQTL analysis and Mendelian Randomization (MR) to identify valuable transposon activities and elite genes. In addition, a convolutional neural network (CNN) model was constructed to evaluate epigenomic effects of transposon-related variations. RESULTS: We identified 35,980 transposon activities among 10 cotton genomes, and the diversity of genomic and epigenomic features was observed among 21 transposon categories. The graphical cotton transposon-related variome was constructed, and 9,614 transposon-related variations with plasticity in the modern cotton cohort were used for eQTL, phenotypic t-test and Mendelian Randomization. 128 genes were identified as gene resources improving fiber length and strength simultaneously. 4 genes were selected from 128 genes to construct the elite gene panel whose utility has been validated in a natural cotton cohort and 2 accessions with phenotypic divergence. Based on the eQTL analysis results, we identified transposon-related variations involved in cotton's environmental adaption and human domestication, providing evidence of their role in cotton's adaption-domestication cooperation. CONCLUSIONS: The cotton transposon-related variome revealed the role of transposon-related variations in modern cotton cultivation, providing genomic resources for cotton molecular breeding.

GhHDZ76, a cotton HD-Zip transcription factor, involved in regulating the initiation and early elongation of cotton fiber development in G. hirsutum.

In this study, the whole HD-Zip family members of G. hirsutum were identified, and GhHDZ76 was classified into the HD-Zip IV subgroup. GhHDZ76 was predominantly expressed in the 0-5 DPA of fiber development stage and localized in the nucleus. Overexpression of GhHDZ76 significantly increased the length and density of trichomes in Arabidopsis thaliana. The fiber length of GhHDZ76 knockout lines by CRISPR/Cas9 was significantly shorter than WT at the early elongation and mature stage, indicating that GhHDZ76 positively regulate the fiber elongation. Scanning electron microscopy showed that the number of ovule surface protrusion of 0 DPA of GhHDZ76 knockout lines was significantly lower than WT, suggesting that GhHDZ76 can also promote the initiation of fiber development. The transcript level of GhWRKY16, GhRDL1, GhEXPA1 and GhMYB25 genes related to fiber initiation and elongation in GhHDZ76 knockout lines were significantly decreased. Yeast two-hybrid and Luciferase complementation imaging (LCI) assays showed that GhHDZ76 can interact with GhWRKY16 directly. As a transcription factor, GhHDZ76 has transcriptional activation activity, which could bind to L1-box elements of the promoters of GhRDL1 and GhEXPA1. Double luciferase reporter assay showed that the GhWRKY16 could enhance the transcriptional activity of GhHDZ76 to pGhRDL1, but it did not promote the transcriptional activity of GhHDZ76 to pGhEXPA1. GhHDZ76 protein may also promote the transcriptional activity of GhWRKY16 to the downstream target gene GhMYB25. Our results provided a new gene resource for fiber development and a theoretical basis for the genetic improvement of cotton fiber quality.

MF-Net: multi-scale feature extraction-integration network for unsupervised deformable registration.

Deformable registration plays a fundamental and crucial role in scenarios such as surgical navigation and image-assisted analysis. While deformable registration methods based on unsupervised learning have shown remarkable success in predicting displacement fields with high accuracy, many existing registration networks are limited by the lack of multi-scale analysis, restricting comprehensive utilization of global and local features in the images. To address this limitation, we propose a novel registration network called multi-scale feature extraction-integration network (MF-Net). First, we propose a multiscale analysis strategy that enables the model to capture global and local semantic information in the image, thus facilitating accurate texture and detail registration. Additionally, we introduce grouped gated inception block (GI-Block) as the basic unit of the feature extractor, enabling the feature extractor to selectively extract quantitative features from images at various resolutions. Comparative experiments demonstrate the superior accuracy of our approach over existing methods.

3D-CAM: a novel context-aware feature extraction framework for neurological disease classification.

In clinical practice and research, the classification and diagnosis of neurological diseases such as Parkinson's Disease (PD) and Multiple System Atrophy (MSA) have long posed a significant challenge. Currently, deep learning, as a cutting-edge technology, has demonstrated immense potential in computer-aided diagnosis of PD and MSA. However, existing methods rely heavily on manually selecting key feature slices and segmenting regions of interest. This not only increases subjectivity and complexity in the classification process but also limits the model's comprehensive analysis of global data features. To address this issue, this paper proposes a novel 3D context-aware modeling framework, named 3D-CAM. It considers 3D contextual information based on an attention mechanism. The framework, utilizing a 2D slicing-based strategy, innovatively integrates a Contextual Information Module and a Location Filtering Module. The Contextual Information Module can be applied to feature maps at any layer, effectively combining features from adjacent slices and utilizing an attention mechanism to focus on crucial features. The Location Filtering Module, on the other hand, is employed in the post-processing phase to filter significant slice segments of classification features. By employing this method in the fully automated classification of PD and MSA, an accuracy of 85.71%, a recall rate of 86.36%, and a precision of 90.48% were achieved. These results not only demonstrates potential for clinical applications, but also provides a novel perspective for medical image diagnosis, thereby offering robust support for accurate diagnosis of neurological diseases.

Fingerprint Finder: Identifying Genomic Fingerprint Sites in Cotton Cohorts for Genetic Analysis and Breeding Advancement.

Genomic data in Gossypium provide numerous data resources for the cotton genomics community. However, to fill the gap between genomic analysis and breeding field work, detecting the featured genomic items of a subset cohort is essential for geneticists. We developed FPFinder v1.0 software to identify a subset of the cohort's fingerprint genomic sites. The FPFinder was developed based on the term frequency-inverse document frequency algorithm. With the short-read sequencing of an elite cotton pedigree, we identified 453 pedigree fingerprint genomic sites and found that these pedigree-featured sites had a role in cotton development. In addition, we applied FPFinder to evaluate the geographical bias of fiber-length-related genomic sites from a modern cotton cohort consisting of 410 accessions. Enriching elite sites in cultivars from the Yangtze River region resulted in the longer fiber length of Yangze River-sourced accessions. Apart from characterizing functional sites, we also identified 12,536 region-specific genomic sites. Combining the transcriptome data of multiple tissues and samples under various abiotic stresses, we found that several region-specific sites contributed to environmental adaptation. In this research, FPFinder revealed the role of the cotton pedigree fingerprint and region-specific sites in cotton development and environmental adaptation, respectively. The FPFinder can be applied broadly in other crops and contribute to genetic breeding in the future.

SELENOK-dependent CD36 palmitoylation regulates microglial functions and Aß phagocytosis.

Amyloid-beta (Aß) is a key factor in the onset and progression of Alzheimer's disease (AD). Selenium (Se) compounds show promise in AD treatment. Here, we revealed that selenoprotein K (SELENOK), a selenoprotein involved in immune regulation and potentially related to AD pathology, plays a critical role in microglial immune response, migration, and phagocytosis. In vivo and in vitro studies corroborated that SELENOK deficiency inhibits microglial Aß phagocytosis, exacerbating cognitive deficits in 5xFAD mice, which are reversed by SELENOK overexpression. Mechanistically, SELENOK is involved in CD36 palmitoylation through DHHC6, regulating CD36 localization to microglial plasma membranes and thus impacting Aß phagocytosis. CD36 palmitoylation was reduced in the brains of patients and mice with AD. Se supplementation promoted SELENOK expression and CD36 palmitoylation, enhancing microglial Aß phagocytosis and mitigating AD progression. We have identified the regulatory mechanisms from Se-dependent selenoproteins to Aß pathology, providing novel insights into potential therapeutic strategies involving Se and selenoproteins.

GhUBC10-2 mediates GhGSTU17 degradation to regulate salt tolerance in cotton (Gossypium hirsutum).

Ubiquitin-conjugating enzyme (UBC) is a crucial component of the ubiquitin-proteasome system, which contributes to plant growth and development. While some UBCs have been identified as potential regulators of abiotic stress responses, the underlying mechanisms of this regulation remain poorly understood. Here, we report a cotton (Gossypium hirsutum) UBC gene, GhUBC10-2, which negatively regulates the salt stress response. We found that the gain of function of GhUBC10-2 in both Arabidopsis (Arabidopsis thaliana) and cotton leads to reduced salinity tolerance. Additionally, GhUBC10-2 interacts with glutathione S-transferase (GST) U17 (GhGSTU17), forming a heterodimeric complex that promotes GhGSTU17 degradation. Intriguingly, GhUBC10-2 can be self-polyubiquitinated, suggesting that it possesses E3-independent activity. Our findings provide new insights into the PTM of plant GST-mediated salt response pathways. Furthermore, we found that the WRKY transcription factor GhWRKY13 binds to the GhUBC10-2 promoter and suppresses its expression under salt conditions. Collectively, our study unveils a regulatory module encompassing GhWRKY13-GhUBC10-2-GhGSTU17, which orchestrates the modulation of reactive oxygen species homeostasis to enhance salt tolerance.

Multiple sequence alignment-based RNA language model and its application to structural inference.

Compared with proteins, DNA and RNA are more difficult languages to interpret because four-letter coded DNA/RNA sequences have less information content than 20-letter coded protein sequences. While BERT (Bidirectional Encoder Representations from Transformers)-like language models have been developed for RNA, they are ineffective at capturing the evolutionary information from homologous sequences because unlike proteins, RNA sequences are less conserved. Here, we have developed an unsupervised multiple sequence alignment-based RNA language model (RNA-MSM) by utilizing homologous sequences from an automatic pipeline, RNAcmap, as it can provide significantly more homologous sequences than manually annotated Rfam. We demonstrate that the resulting unsupervised, two-dimensional attention maps and one-dimensional embeddings from RNA-MSM contain structural information. In fact, they can be directly mapped with high accuracy to 2D base pairing probabilities and 1D solvent accessibilities, respectively. Further fine-tuning led to significantly improved performance on these two downstream tasks compared with existing state-of-the-art techniques including SPOT-RNA2 and RNAsnap2. By comparison, RNA-FM, a BERT-based RNA language model, performs worse than one-hot encoding with its embedding in base pair and solvent-accessible surface area prediction. We anticipate that the pre-trained RNA-MSM model can be fine-tuned on many other tasks related to RNA structure and function.

Cotton pedigree genome reveals restriction of cultivar-driven strategy in cotton breeding.

BACKGROUND: Many elite genes have been identified from the available cotton genomic data, providing various genetic resources for gene-driven breeding. However, backbone cultivar-driven breeding is the most widely applied strategy. Revealing the genetic basis of cultivar-driven strategy's restriction is crucial for transition of cotton breeding strategy. RESULT: CRI12 is a backbone cultivar in cultivar-driven breeding. Here we sequence the pedigree of CRI12 using Nanopore long-read sequencing. We construct a graphical pedigree genome using the high-quality CRI12 genome and 13,138 structural variations within 20 different pedigree members. We find that low hereditary stability of elite segments in backbone cultivars is a drawback of cultivar-driven strategy. We also identify 623 functional segments in CRI12 for multiple agronomic traits in presence and absence variation-based genome-wide association study on three cohorts. We demonstrate that 25 deleterious segments are responsible for the geographical divergence of cotton in pathogen resistance. We also characterize an elite pathogen-resistant gene (GhKHCP) utilized in modern cotton breeding. In addition, we identify 386 pedigree fingerprint segments by comparing the segments of the CRI12 pedigree with those of a large cotton population. CONCLUSION: We characterize the genetic patterns of functional segments in the pedigree of CRI12 using graphical genome method, revealing restrictions of cultivar-driven strategies in cotton breeding. These findings provide theoretical support for transitioning from cultivar-driven to gene-driven strategy in cotton breeding.

Identification and molecular evolution of the GLX genes in 21 plant species: a focus on the Gossypium hirsutum.

BACKGROUND: The glyoxalase system includes glyoxalase I (GLXI), glyoxalase II (GLXII) and glyoxalase III (GLXIII), which are responsible for methylglyoxal (MG) detoxification and involved in abiotic stress responses such as drought, salinity and heavy metal. RESULTS: In this study, a total of 620 GLX family genes were identified from 21 different plant species. The results of evolutionary analysis showed that GLX genes exist in all species from lower plants to higher plants, inferring that GLX genes might be important for plants, and GLXI and GLXII account for the majority. In addition, motif showed an expanding trend in the process of evolution. The analysis of cis-acting elements in 21 different plant species showed that the promoter region of the GLX genes were rich in phytohormones and biotic and abiotic stress-related elements, indicating that GLX genes can participate in a variety of life processes. In cotton, GLXs could be divided into two groups and most GLXIs distributed in group I, GLXIIs and GLXIIIs mainly belonged to group II, indicating that there are more similarities between GLXII and GLXIII in cotton evolution. The transcriptome data analysis and quantitative real-time PCR analysis (qRT-PCR) show that some members of GLX family would respond to high temperature treatment in G.hirsutum. The protein interaction network of GLXs in G.hirsutum implied that most members can participate in various life processes through protein interactions. CONCLUSIONS: The results elucidated the evolutionary history of GLX family genes in plants and lay the foundation for their functions analysis in cotton.

Whey protein powder with milk fat globule membrane attenuates Alzheimer's disease pathology in 3×Tg-AD mice by modulating neuroinflammation through the peroxisome proliferator-activated receptor γ signaling pathway.

Whey protein powder (PP), which is mainly derived from bovine milk, is rich in milk fat globule membrane (MFGM). The MGFM has been shown to play a role in promoting neuronal development and cognition in the infant brain. However, its role in Alzheimer's disease (AD) has not been elucidated. Here, we showed that the cognitive ability of 3×Tg-AD mice (a triple-transgenic mouse model of AD) could be improved by feeding PP to mice for 3 mo. In addition, PP ameliorated amyloid peptide deposition and tau hyperphosphorylation in the brains of AD mice. We found that PP could alleviate AD pathology by inhibiting neuroinflammation through the peroxisome proliferator-activated receptor γ (PPARγ)-nuclear factor-κB signaling pathway in the brains of AD mice. Our study revealed an unexpected role of PP in regulating the neuroinflammatory pathology of AD in a mouse model.

Comparative Transcriptome Analysis Revealed Key Genes Regulating Gossypol Synthesis in Tetraploid Cultivated Cotton.

Tetraploid cultivated cotton (Gossypium spp.) produces cottonseeds rich in protein and oil. Gossypol and related terpenoids, stored in the pigment glands of cottonseeds, are toxic to human beings and monogastric animals. However, a comprehensive understanding of the genetic basis of gossypol and gland formation is still lacking. We performed a comprehensive transcriptome analysis of four glanded versus two glandless tetraploid cultivars distributed in Gossypium hirsutum and Gossypium barbadense. A weighted gene co-expression network analysis (WGCNA) based on 431 common differentially expressed genes (DEGs) uncovered a candidate module that was strongly associated with the reduction in or disappearance of gossypol and pigment glands. Further, the co-expression network helped us to focus on 29 hub genes, which played key roles in the regulation of related genes in the candidate module. The present study contributes to our understanding of the genetic basis of gossypol and gland formation and serves as a rich potential source for breeding cotton cultivars with gossypol-rich plants and gossypol-free cottonseed, which is beneficial for improving food safety, environmental protection, and economic gains of tetraploid cultivated cotton.

Weakly-Supervised 3D Spatial Reasoning for Text-Based Visual Question Answering.

Text-based Visual Question Answering (TextVQA) aims to produce correct answers for given questions about the images with multiple scene texts. In most cases, the texts naturally attach to the surface of the objects. Therefore, spatial reasoning between texts and objects is crucial in TextVQA. However, existing approaches are constrained within 2D spatial information learned from the input images and rely on transformer-based architectures to reason implicitly during the fusion process. Under this setting, these 2D spatial reasoning approaches cannot distinguish the fine-grained spatial relations between visual objects and scene texts on the same image plane, thereby impairing the interpretability and performance of TextVQA models. In this paper, we introduce 3D geometric information into the spatial reasoning process to capture the contextual knowledge of key objects step-by-step. Specifically, (i) we propose a relation prediction module for accurately locating the region of interest of critical objects; (ii) we design a depth-aware attention calibration module for calibrating the OCR tokens' attention according to critical objects. Extensive experiments show that our method achieves state-of-the-art performance on TextVQA and ST-VQA datasets. More encouragingly, our model surpasses others by clear margins of 5.7% and 12.1% on questions that involve spatial reasoning in TextVQA and ST-VQA valid split. Besides, we also verify the generalizability of our model on the text-based image captioning task.

Multifunctional theranostic carbazole-based cyanine for real-time imaging of amyloid-ß and therapeutic treatment of multiple pathologies in Alzheimer's disease.

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder characterized by the synaptic and neuronal loss, which results in cognitive impairment in particular learning and memory. Currently, AD is incurable and no single confirmative test can clinically be used to diagnose AD. In light of the complex and multifactorial nature of AD etiology, the development of multifunctional/multi-target drugs that act on multiple pathological pathways and mechanisms shows great therapeutic potential for intervention of this devastating disease. We report herein a multifunctional theranostic cyanine, SLCOOH, which serves not only as a highly sensitive fluorescent probe for real-time imaging of amyloid-ß (Aß) contents in different age groups of transgenic (Tg) AD mice but also as an effective therapeutic agent for early AD intervention via multiple pathological targets in the AD mouse model. Remarkably, treatment with SLCOOH gives rise to multiple therapeutic benefits, including the amelioration of cognitive decline, a reduction in Aß levels, a decrease in hyperphosphorylated tau proteins and tau depositions, and the alleviation of synaptic loss and dysfunctions in young triple Tg AD mice. Our results have demonstrated that in addition to superior Aß imaging capability, SLCOOH exhibits versatile and effective multiple modes of drug action, signifying outstanding therapeutic potential to treat early onset AD. Our work also paves the way for the development of effective Aß-targeted theranostic agents for AD.

Automatic Kidney Segmentation Method Based on an Enhanced Generative Adversarial Network.

When deciding on a kidney tumor's diagnosis and treatment, it is critical to take its morphometry into account. It is challenging to undertake a quantitative analysis of the association between kidney tumor morphology and clinical outcomes due to a paucity of data and the need for the time-consuming manual measurement of imaging variables. To address this issue, an autonomous kidney segmentation technique, namely SegTGAN, is proposed in this paper, which is based on a conventional generative adversarial network model. Its core framework includes a discriminator network with multi-scale feature extraction and a fully convolutional generator network made up of densely linked blocks. For qualitative and quantitative comparisons with the SegTGAN technique, the widely used and related medical image segmentation networks U-Net, FCN, and SegAN are used. The experimental results show that the Dice similarity coefficient (DSC), volumetric overlap error (VOE), accuracy (ACC), and average surface distance (ASD) of SegTGAN on the Kits19 dataset reach 92.28%, 16.17%, 97.28%, and 0.61 mm, respectively. SegTGAN outscores all the other neural networks, which indicates that our proposed model has the potential to improve the accuracy of CT-based kidney segmentation.

Functional characterization of the GhNRT2.1e gene reveals its significant role in improving nitrogen use efficiency in Gossypium hirsutum.

Background: Nitrate is the primary type of nitrogen available to plants, which is absorbed and transported by nitrate transporter 2 (NRT2) at low nitrate conditions. Methods: Genome-wide identification of NRT2 genes in G. hirsutum was performed. Gene expression patterns were revealed using RNA-seq and qRT-PCR. Gene functions were characterized using overexpression in A. thaliana and silencing in G. hirsutum. Protein interactions were verified by yeast two-hybrid and luciferase complementation imaging (LCI) assays. Results: We identified 14, 14, seven, and seven NRT2 proteins in G. hirsutum, G. barbadense, G. raimondii, and G. arboreum. Most NRT2 proteins were predicted in the plasma membrane. The NRT2 genes were classified into four distinct groups through evolutionary relationships, with members of the same group similar in conserved motifs and gene structure. The promoter regions of NRT2 genes included many elements related to growth regulation, phytohormones, and abiotic stresses. Tissue expression pattern results revealed that most GhNRT2 genes were specifically expressed in roots. Under low nitrate conditions, GhNRT2 genes exhibited different expression levels, with GhNRT2.1e being the most up-regulated. Arabidopsis plants overexpressing GhNRT2.1e exhibited increased biomass, nitrogen and nitrate accumulation, nitrogen uptake and utilization efficiency, nitrogen-metabolizing enzyme activity, and amino acid content under low nitrate conditions. In addition, GhNRT2.1e-silenced plants exhibited suppressed nitrate uptake and accumulation, hampered plant growth, affected nitrogen metabolism processes, and reduced tolerance to low nitrate. The results showed that GhNRT2.1e could promote nitrate uptake and transport under low nitrate conditions, thus effectively increasing nitrogen use efficiency (NUE). We found that GhNRT2.1e interacts with GhNAR2.1 by yeast two-hybrid and LCI assays. Discussion: Our research lays the foundation to increase NUE and cultivate new cotton varieties with efficient nitrogen use.

Semi-Supervised CT Lesion Segmentation Using Uncertainty-Based Data Pairing and SwapMix.

Semi-supervised learning (SSL) methods show their powerful performance to deal with the issue of data shortage in the field of medical image segmentation. However, existing SSL methods still suffer from the problem of unreliable predictions on unannotated data due to the lack of manual annotations for them. In this paper, we propose an unreliability-diluted consistency training (UDiCT) mechanism to dilute the unreliability in SSL by assembling reliable annotated data into unreliable unannotated data. Specifically, we first propose an uncertainty-based data pairing module to pair annotated data with unannotated data based on a complementary uncertainty pairing rule, which avoids two hard samples being paired off. Secondly, we develop SwapMix, a mixed sample data augmentation method, to integrate annotated data into unannotated data for training our model in a low-unreliability manner. Finally, UDiCT is trained by minimizing a supervised loss and an unreliability-diluted consistency loss, which makes our model robust to diverse backgrounds. Extensive experiments on three chest CT datasets show the effectiveness of our method for semi-supervised CT lesion segmentation.

Identification of MYB gene family and functional analysis of GhMYB4 in cotton (Gossypium spp.).

Myeloblastosis (MYB) transcription factors (TFs) form a large gene family involved in a variety of biological processes in plants. Little is known about their roles in the development of cotton pigment glands. In this study, 646 MYB members were identified in Gossypium hirsutum genome and phylogenetic classification was analyzed. Evolution analysis revealed assymetric evolution of GhMYBs during polyploidization and sequence divergence of MYBs in G. hirustum was preferentially happend in D sub-genome. WGCNA (weighted gene co-expression network analysis) showed that four modules had potential relationship with gland development or gossypol biosynthesis in cotton. Eight differentially expressed GhMYB genes were identified by screening transcriptome data of three pairs of glanded and glandless cotton lines. Of these, four were selected as candidate genes for cotton pigment gland formation or gossypol biosynthesis by qRT-PCR assay. Silencing of GH_A11G1361 (GhMYB4) downregulated expression of multiple genes in gossypol biosynthesis pathway, indicating it could be involved in gossypol biosynthesis. The potential protein interaction network suggests that several MYBs may have indirect interaction with GhMYC2-like, a key regulator of pigment gland formation. Our study was the systematic analysis of MYB genes in cotton pigment gland development, providing candidate genes for further study on the roles of cotton MYB genes in pigment gland formation, gossypol biosynthesis and future crop plant improvement.

Different Effects and Mechanisms of Selenium Compounds in Improving Pathology in Alzheimer's Disease.

Owing to the strong antioxidant capacity of selenium (Se) in vivo, a variety of Se compounds have been shown to have great potential for improving the main pathologies and cognitive impairment in Alzheimer's disease (AD) models. However, the differences in the anti-AD effects and mechanisms of different Se compounds are still unclear. Theoretically, the absorption and metabolism of different forms of Se in the body vary, which directly determines the diversification of downstream regulatory pathways. In this study, low doses of Se-methylselenocysteine (SMC), selenomethionine (SeM), or sodium selenate (SeNa) were administered to triple transgenic AD (3× Tg-AD) mice for short time periods. AD pathology, activities of selenoenzymes, and metabolic profiles in the brain were studied to explore the similarities and differences in the anti-AD effects and mechanisms of the three Se compounds. We found that all of these Se compounds significantly increased Se levels and antioxidant capacity, regulated amino acid metabolism, and ameliorated synaptic deficits, thus improving the cognitive capacity of AD mice. Importantly, SMC preferentially increased the expression and activity of thioredoxin reductase and reduced tau phosphorylation by inhibiting glycogen synthase kinase-3 beta (GSK-3ß) activity. Glutathione peroxidase 1 (GPx1), the selenoenzyme most affected by SeM, decreased amyloid beta production and improved mitochondrial function. SeNa improved methionine sulfoxide reductase B1 (MsrB1) expression, reflected in AD pathology as promoting the expression of synaptic proteins and restoring synaptic deficits. Herein, we reveal the differences and mechanisms by which different Se compounds improve multiple pathologies of AD and provide novel insights into the targeted administration of Se-containing drugs in the treatment of AD.

Identification and Characterization of Phytocyanin Family Genes in Cotton Genomes.

Phytocyanins (PCs) are a class of plant-specific blue copper proteins that have been demonstrated to play a role in electron transport and plant development. Through analysis of the copper ligand residues, spectroscopic properties, and domain architecture of the protein, PCs have been grouped into four subfamilies: uclacyanins (UCs), stellacyanins (SCs), plantacyanins (PLCs), and early nodulin-like proteins (ENODLs). The present study aimed to identify and characterise the PCs present in three distinct cotton species (Gossypium hirsutum, Gossyium arboreum, and Gossypium raimondii) through the identification of 98, 63, and 69 genes respectively. We grouped PCs into four clades by using bioinformatics analysis and sequence alignment, which exhibit variations in gene structure and motif distribution. PCs are distributed across all chromosomes in each of the three species, with varying numbers of exons per gene and multiple conserved motifs, and with a minimum of 1 and maximum of 11 exons found on one gene. Transcriptomic data and qRT-PCR analysis revealed that two highly differentiated PC genes were expressed at the fibre initiation stage, while three highly differentiated PCs were expressed at the fibre elongation stage. These findings serve as a foundation for further investigations aimed at understanding the contribution of this gene family in cotton fibre production.