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Early-onset Alzheimer's disease (EOAD) is sporadic, highly heterogeneous, and its underlying pathogenic mechanisms remain largely elusive. Proteomics research aims to uncover the biological processes and key proteins involved in disease progression. However, no proteomic studies to date have specifically focused on EOAD brain tissue. We integrated proteomic data from brain tissues of two Alzheimer's disease (AD) cohorts and constructed a protein co-expression network using weighted gene co-expression network analysis (WGCNA). We identified modules associated with EOAD, conducted functional enrichment analysis to understand the biological processes involved in EOAD, and pinpointed potential key proteins within the core modules most closely linked to AD pathology. In this study, we identified a total of 2,749 proteins associated with EOAD. Through protein co-expression network analysis, we discovered 41 distinct co-expression modules. Notably, the proteins within the core module most closely linked to AD pathology were significantly enriched in neutrophil degranulation. Additionally, we identified two potential key proteins within this core module that have not been previously reported in AD and validated their expression levels in 5xFAD mice. In summary, through a protein co-expression network analysis, we identified EOAD-related biological processes and molecular pathways, and screened and validated two key proteins, ERBB2IP and LSP1. These proteins may play an important role in the progression of EOAD, suggesting they could serve as potential therapeutic targets for the disease.

The interfacial effect is important for anodes of transition metal dichalcogenides (TMDs) to achieve superior lithium-ion storage performance. In this paper, a MoS2/FeS2 heterojunction is synthesized by a simple hydrothermal reaction to construct the interface effect, and the heterostructure introduces an inherent electric field that accelerates the de-embedding process of lithium ions, improves the electron transfer capability, and effectively mitigates volume expansion. XPS analysis confirms evident chemical interaction between MoS2 and FeS2 via an interfacial covalent bond (Mo–S–Fe). This MoS2/FeS2 anode shows a distinct interfacial effect for efficient interatomic electron migration. The electrochemical performance demonstrated that the discharge capacity can reach up to 1217.8 mA h g−1 at 0.1 A g−1 after 200 cycles, with a capacity retention rate of 72.9%. After 2000 cycles, the capacity retention is about 61.6% at 1.0 A g−1, and the discharge capacity can still reach 638.9 mA h g−1. Electrochemical kinetic analysis indicated an enhanced pseudocapacitance contribution and that the MoS2/FeS2 had sufficient adsorption of lithium ions. This paper therefore argues that this interfacial engineering is an effective solution for designing sulfide-based anodes with good electrochemical properties.

BackgroundAtherosclerosis is a significant contributor to cardiovascular disease, and conventional diagnostic methods frequently fall short in the timely and accurate detection of early-stage atherosclerosis. Abnormal lipid metabolism plays a critical role in the development of atherosclerosis. Consequently, the identification of new diagnostic markers is essential for the precise diagnosis of this condition.MethodThe datasets related to atherosclerosis utilized in this research were obtained from the GEO database (GSE2470, GSE24495, GSE100927 and GSE43292). The ssGSEA technique was first utilized to assess lipid metabolism scores in samples affected by atherosclerosis, thereby aiding in the discovery of important regulatory genes linked to lipid metabolism via WGCNA. Following this, differential expression analysis and functional evaluations were carried out, after which various machine learning approaches were employed to determine significant diagnostic genes for atherosclerosis. A diagnostic model was then developed and validated through several machine learning algorithms. Furthermore, molecular docking studies were conducted to analyze the binding affinity of these key markers with therapeutic agents for atherosclerosis. The ssGSEA technique was also used to measure immune cell scores in atherosclerotic samples, aiding the exploration of the connection between key diagnostic markers and immune cells. Finally, the expression variations of the identified pivotal genes were confirmed through experimental validation.ResultWGCNA identified 302 lipid metabolism-related genes in atherosclerotic samples, and functional analysis revealed that these genes are associated with multiple immune pathways. Through further differential analysis and screening using machine learning algorithms, APLNR, PCDH12, PODXL, SLC40A1, TM4SF18, and TNFRSF25 were identified as key diagnostic genes for atherosclerosis. The diagnostic model we constructed was confirmed to predict the occurrence of atherosclerosis with high accuracy, and molecular docking studies indicated that these six key diagnostic genes have potential as drug targets. Additionally, the ssGSEA algorithm further validated the association of these diagnostic genes with various immune cells. Finally, the expression levels of these six genes were experimentally confirmed.ConclusionOur study introduces novel lipid metabolism-related diagnostic markers for atherosclerosis and emphasizes their potential as immune-related drug targets. This research provides a valuable approach for the predictive diagnosis and targeted therapy of atherosclerosis.

The rational design of the heterogeneous interfaces enables precise adjustment of the electronic structure and optimization of the kinetics for electron/ion migration in energy storage materials. In this work, the built-in electric field is introduced to the iron-based anode material (Fe2O3@TiO2) through the well-designed heterostructure. This model serves as an ideal platform for comprehending the atomic-level optimization of electron transfer in advanced lithium-ion batteries (LIBs). As a result, the core-shell Fe2O3@TiO2 delivers a remarkable discharge capacity of 1342 mAh g−1 and an extraordinary capacity retention of 82.7% at 0.1 A g−1 after 300 cycles. Fe2O3@TiO2 shows an excellent rate performance from 0.1 A g−1 to 4.0 A g−1. Further, the discharge capacity of Fe2O3@TiO2 reached 736 mAh g−1 at 1.0 A g−1 after 2000 cycles, and the corresponding capacity retention is 83.62%. The heterostructure forms a conventional p-n junction, successfully constructing the built-in electric field and lithium-ion reservoir. The kinetic analysis demonstrates that Fe2O3@TiO2 displays high pseudocapacitance behavior (77.8%) and fast lithium-ion reaction kinetics. The capability of heterointerface engineering to optimize electrochemical reaction kinetics offers novel insights for constructing high-performance iron-based anodes for LIBs.

This paper introduces an arithmetic data file, a key technology for data processing in a ternary optical computer (TOC). The physical form of the ternary optical processor and its data processing characteristics are analyzed. Based on this analysis, the compution-data is constructed, and research is carried out on the format of the compution-data, its generation method, and the expansion of high-level languages transmitted to the ternary optical processor. The calculation rules and the raw data for the ternary optical computer are organized into a file that conforms to the calculation characteristics of the computer. A data processing mechanism based on the compution-data is proposed. Finally, an experimental test was conducted on the platform of a ternary optical computer using specific examples. The results showed that by organizing and transmitting data through the compution-data, the ternary optical computer could fully utilize its computational advantages in data processing while shielding the underlying complex hardware processing. This makes it convenient for users to apply this new type of computer. This data processing mechanism can offer a novel perspective for other heterogeneous systems in data processing.

Background Organic acids and anthocyanins are the most important compounds for the flavor and nutritional quality of citrus fruit. However, there are few reports on the involvement of co-regulation of citrate and anthocyanin metabolism. Here, we performed a comparative transcriptome analysis to elucidate the genes and pathways involved in both citrate and anthocyanin accumulation in postharvest citrus fruit with ‘Tarocco’ blood orange (TBO; high accumulation) and ‘Bingtangcheng’ sweet orange (BTSO; low accumulation). Results A robust core set of 825 DEGs were found to be temporally associated with citrate and anthocyanin accumulation throughout the storage period through transcriptome analysis. Further according to the results of weighted gene coexpression correlation network analysis (WGCNA), the turquoise and brown module was highly positively correlated with both of the content of citrate and anthocyanin, and p-type ATPase ( PH8 ), phosphoenolpyruvate carboxylase kinase ( PEPCK ), chalcone isomerase ( CHI ), flavanone 3-hydroxylase ( F3H ), flavonoid 3’-hydroxylase ( F3’H ) and glutathione S transferase ( GST ) were considered key structural genes. Moreover, MYB family transcription factor ( PH4 ), Zinc finger PHD-type transcription factor ( CHR4 , HAC12 ), Zinc finger SWIM-type transcription factor ( FAR1 ) and Zinc finger C3H1-type transcription factor ( ATC3H64 ) were considered hub genes related to these structural genes. Further qRT-PCR analysis verified that these transcription factors were highly expressed in TBO fruit and their expression profiles were significantly positively correlated with the structural genes of citrate and anthocyanin metabolism as well as the content of citrate and anthocyanin content. Conclusions The findings suggest that the CHR4, FAR1, ATC3H64 and HAC12 may be the new transcription regulators participate in controlling the level of citrate and anthocyanin in postharvest TBO fruit in addition to PH4. These results may providing new insight into the regulation mechanism of citrate and anthocyanin accumulation in citrus fruit.

Three-amino-acid-loop-extension (TALE) transcription factors comprise one of the largest gene families in plants, in which they contribute to regulation of a wide variety of biological processes, including plant growth and development, as well as governing stress responses. Although sweet orange ( Citrus sinensis ) is among the most commercially important fruit crops cultivated worldwide, there have been relatively few functional studies on TALE genes in this species. In this study, we investigated 18 CsTALE gene family members with respect to their phylogeny, physicochemical properties, conserved motif/domain sequences, gene structures, chromosomal location, cis -acting regulatory elements, and protein–protein interactions (PPIs). These CsTALE genes were classified into two subfamilies based on sequence homology and phylogenetic analyses, and the classification was equally strongly supported by the highly conserved gene structures and motif/domain compositions. CsTALEs were found to be unevenly distributed on the chromosomes, and duplication analysis revealed that segmental duplication and purifying selection have been major driving force in the evolution of these genes. Expression profile analysis indicated that CsTALE genes exhibit a discernible spatial expression pattern in different tissues and differing expression patterns in response to different biotic/abiotic stresses. Of the 18 CsTALE genes examined, 10 were found to be responsive to high temperature, four to low temperature, eight to salt, and four to wounding. Moreover, the expression of CsTALE3/8/12/16 was induced in response to infection with the fungal pathogen Diaporthe citri and bacterial pathogen Candidatus Liberibacter asiaticus, whereas the expression of CsTALE15/17 was strongly suppressed. The transcriptional activity of Cs TALE proteins was also verified in yeast, with yeast two-hybrid assays indicating that CsTALE3/CsTALE8, CsTALE3/CsTALE11, CsTALE10/CsTALE12, CsTALE14/CsTALE8, CsTALE14/CsTALE11 can form respective heterodimers. The findings of this study could lay the foundations for elucidating the biological functions of the TALE family genes in sweet orange and contribute to the breeding of stress-tolerant plants.

Global climate change is leading to more frequent extreme cold events, underscoring the need to study citrus cold tolerance to support breeding and enable potential northward expansion of citrus cultivation. In this study, the ‘Miyagawa’ wild type and its cold-tolerant mutant were selected for systematic comparison across cold-resistant phenotypes, leaf tissue structure, physiological and biochemical characteristics, and Cor8 gene expression. The mutant exhibited 50% lower relative conductivity and malondialdehyde (MDA) content under −6 °C stress compared to the wild type, indicating reduced membrane damage. Antioxidant enzyme activities were significantly higher in the mutant: superoxide dismutase (SOD) activity increased by 10–30%, peroxidase (POD) by 28%, and catalase (CAT) by up to 2-fold. Proline content was 57% higher in the mutant at peak levels, supporting stronger osmotic regulation. Moreover, Cor8 gene expression in the mutant was up to 2.98 times higher than in the wild type during natural overwintering. These findings confirm that the ‘Miyagawa’ mutant possesses distinct physiological, anatomical, and molecular advantages for low-temperature adaptation and provides valuable germplasm for breeding cold-tolerant citrus varieties.

Perineural invasion (PNI), the neoplastic invasion of nerves, is an often overlooked pathological phenomenon in cervical cancer that is associated with poor clinical outcomes. The occurrence of PNI in cervical cancer patients has limited the promotion of Type C1 surgery. Preoperative prediction of the PNI can help identify suitable patients for Type C1 surgery. However, there is a lack of appropriate preoperative diagnostic methods for PNI, and its pathogenesis remains largely unknown. Here, we dissect the neural innervation of the cervix, analyze the molecular mechanisms underlying the occurrence of PNI, and explore suitable preoperative diagnostic methods for PNI to advance the identification and treatment of this ominous cancer phenotype.

Soybean pod count is a crucial aspect of soybean plant phenotyping, offering valuable reference information for breeding and planting management. Traditional manual counting methods are not only costly but also prone to errors. Existing detection-based soybean pod counting methods face challenges due to the crowded and uneven distribution of soybean pods on the plants. To tackle this issue, we propose a Soybean Pod Counting Network (SPCN) for accurate soybean pod counting. SPCN is a density map-based architecture based on Hybrid Dilated Convolution (HDC) strategy and attention mechanism for feature extraction, using the Unbalanced Optimal Transport (UOT) loss function for supervising density map generation. Additionally, we introduce a new diverse dataset, BeanCount-1500, comprising of 24,684 images of 316 soybean varieties with various backgrounds and lighting conditions. Extensive experiments on BeanCount-1500 demonstrate the advantages of SPCN in soybean pod counting with an Mean Absolute Error(MAE) and an Mean Squared Error(MSE) of 4.37 and 6.45, respectively, significantly outperforming the current competing method by a substantial margin. Its excellent performance on the Renshou2021 dataset further confirms its outstanding generalization potential. Overall, the proposed method can provide technical support for intelligent breeding and planting management of soybean, promoting the digital and precise management of agriculture in general.