Identification and validation of disulfidptosis-related gene signatures and their subtype in diabetic nephropathy

Background: Diabetic nephropathy (DN) is the most common complication of diabetes, and its pathogenesis is complex involving a variety of programmed cell death, inflammatory responses, and autophagy mechanisms. Disulfidptosis is a newly discovered mechanism of cell death. There are little studies about the role of disulfidptosis on DN.Methods: First, we obtained the data required for this study from the GeneCards database, the Nephroseq v5 database, and the GEO database. Through differential analysis, we obtained differential disulfidptosis-related genes. At the same time, through WGCNA analysis, we obtained key module genes in DN patients. The obtained intersecting genes were further screened by Lasso as well as SVM-RFE. By intersecting the results of the two, we ended up with a key gene for diabetic nephropathy. The diagnostic performance and expression of key genes were verified by the GSE30528, GSE30529, GSE96804, and Nephroseq v5 datasets. Using clinical information from the Nephroseq v5 database, we investigated the correlation between the expression of key genes and estimated glomerular filtration rate (eGFR) and serum creatinine content. Next, we constructed a nomogram and analyzed the immune microenvironment of patients with DN. The identification of subtypes facilitates individualized treatment of patients with DN.Results: We obtained 91 differential disulfidptosis-related genes. Through WGCNA analysis, we obtained 39 key module genes in DN patients. Taking the intersection of the two, we preliminarily screened 20 genes characteristic of DN. Through correlation analysis, we found that these 20 genes are positively correlated with each other. Further screening by Lasso and SVM-RFE algorithms and intersecting the results of the two, we identified CXCL6, CD48, C1QB, and COL6A3 as key genes in DN. Clinical correlation analysis found that the expression levels of key genes were closely related to eGFR. Immune cell infiltration is higher in samples from patients with DN than in normal samples.Conclusion: We identified and validated 4 DN key genes from disulfidptosis-related genes that CXCL6, CD48, C1QB, and COL6A3 may be key genes that promote the onset of DN and are closely related to the eGFR and immune cell infiltrated in the kidney tissue

Allelic effects and variations for key bread-making quality genes in bread wheat using high-throughput molecular markers

We developed and validated high-throughput Kompetitive Allele-Specific PCR (KASP) assays for key genes underpinning bread-making quality, including the wbm gene on chromosome 7AL and over-expressed glutenin Bx7 (Glu-B1al) gene. Additionally, we used pre-existing KASP assay for Sec1 (1B.1R translocation) gene on chromosome 1B. The newly developed KASP assays were compared with gel-based markers for reliability and phenotypically validated in a diversity panel for Mixograph, Rapid Visco Analyzer (RVA) and Mixolab traits. Genotypes carrying the 1B.1R translocation had significantly lower Mixolab parameters than those without the translocation. Similarly, superior effects of the wbm+ and Bx7 alleles on Mixograph and RVA properties and their extremely low frequencies in global wheat collections supported the idea of using these genes for bread-making quality improvement. The allele frequencies of wbm+ and Bx7 were extremely low in historical Chinese and CIMMYT wheat germplasm, but were relatively higher in synthetic hexaploid wheats and their breeding derivatives. In both the Vavilov and Watkins global landrace collections, the frequency of wbm+ was 6.4 and 3.5%, and frequency of Bx7 was 3.2% and 7.0%, respectively. The high-throughput marker resources and large-scale global germplasm screening provided further opportunities to exploit these genes in wheat breeding to enhance bread-making quality

Optical wood with switchable solar transmittance for all-round thermal management

Technologies enabling passive daytime radiative cooling and daylight harvesting are highly relevant for energy-efficient buildings. Despite recent progress demonstrated with passively cooling polymer coatings, however, it remains challenging to combine also a passive heat gain mechanism into a single substrate for all-round thermal management. Herein, we developed an optical wood (OW) with switchable transmittance of solar irradiation enabled by the hierarchically porous structure, ultralow absorption in solar spectrum and high infrared absorption of cellulose nanofibers. After delignification, the OW shows a high solar reflectance (94.9%) in the visible and high broadband emissivity (0.93) in the infrared region (2.5-25 $\mu$m). Owing to the exceptional mass transport of its aligned cellulose nanofibers, OW can quickly switch to a new highly transparent state following phenylethanol impregnation. The solar transmittance of optical wood (OW-II state) can reach 68.4% from 250 to 2500 nm. The switchable OW exhibits efficient radiative cooling to 4.5 {\deg}C below ambient temperature in summer (81.4 W m$^{-2}$ cooling power), and daylight heating to 5.6 {\deg}C above the temperature of natural wood in winter (heating power 229.5 W m$^{-2}$), suggesting its promising role as a low-cost and sustainable solution to all-season thermal management applications.Comment: accepted version of the manuscript published on Composites Part B: Engineerin

Optimal Filters with Multiple Packet Losses and its Application in Wireless Sensor Networks

This paper is concerned with the filtering problem for both discrete-time stochastic linear (DTSL) systems and discrete-time stochastic nonlinear (DTSN) systems. In DTSL systems, an linear optimal filter with multiple packet losses is designed based on the orthogonal principle analysis approach over unreliable wireless sensor networks (WSNs), and the experience result verifies feasibility and effectiveness of the proposed linear filter; in DTSN systems, an extended minimum variance filter with multiple packet losses is derived, and the filter is extended to the nonlinear case by the first order Taylor series approximation, which is successfully applied to unreliable WSNs. An application example is given and the corresponding simulation results show that, compared with extended Kalman filter (EKF), the proposed extended minimum variance filter is feasible and effective in WSNs

Telomere maintenance-related genes are important for survival prediction and subtype identification in bladder cancer

Background: Bladder cancer ranks among the top three in the urology field for both morbidity and mortality. Telomere maintenance-related genes are closely related to the development and progression of bladder cancer, and approximately 60%–80% of mutated telomere maintenance genes can usually be found in patients with bladder cancer.Methods: Telomere maintenance-related gene expression profiles were obtained through limma R packages. Of the 359 differential genes screened, 17 prognostically relevant ones were obtained by univariate independent prognostic analysis, and then analysed by LASSO regression. The best result was selected to output the model formula, and 11 model-related genes were obtained. The TCGA cohort was used as the internal group and the GEO dataset as the external group, to externally validate the model. Then, the HPA database was used to query the immunohistochemistry of the 11 model genes. Integrating model scoring with clinical information, we drew a nomogram. Concomitantly, we conducted an in-depth analysis of the immune profile and drug sensitivity of the bladder cancer. Referring to the matrix heatmap, delta area plot, consistency cumulative distribution function plot, and tracking plot, we further divided the sample into two subtypes and delved into both.Results: Using bioinformatics, we obtained a prognostic model of telomere maintenance-related genes. Through verification with the internal and the external groups, we believe that the model can steadily predict the survival of patients with bladder cancer. Through the HPA database, we found that three genes, namely ABCC9, AHNAK, and DIP2C, had low expression in patients with tumours, and eight other genes—PLOD1, SLC3A2, RUNX2, RAD9A, CHMP4C, DARS2, CLIC3, and POU5F1—were highly expressed in patients with tumours. The model had accurate predictive power for populations with different clinicopathological features. Through the nomogram, we could easily assess the survival rate of patients. Clinicians can formulate targeted diagnosis and treatment plans for patients based on the prediction results of patient survival, immunoassays, and drug susceptibility analysis. Different subtypes help to further subdivide patients for better treatment purposes.Conclusion: According to the results obtained by the nomogram in this study, combined with the results of patient immune-analysis and drug susceptibility analysis, clinicians can formulate diagnosis and personalized treatment plans for patients. Different subtypes can be used to further subdivide the patient for a more precise treatment plan

Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment

Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer’s disease (AD). Identifying MCI subjects who are at high risk of converting to AD is crucial for effective treatments. In this study, a deep learning approach based on convolutional neural networks (CNN), is designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data. First, MRI images are prepared with age-correction and other processing. Second, local patches, which are assembled into 2.5 dimensions, are extracted from these images. Then, the patches from AD and normal controls (NC) are used to train a CNN to identify deep learning features of MCI subjects. After that, structural brain image features are mined with FreeSurfer to assist CNN. Finally, both types of features are fed into an extreme learning machine classifier to predict the AD conversion. The proposed approach is validated on the standardized MRI datasets from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project. This approach achieves an accuracy of 79.9% and an area under the receiver operating characteristic curve (AUC) of 86.1% in leave-one-out cross validations. Compared with other state-of-the-art methods, the proposed one outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity. Results demonstrate great potentials of the proposed CNN-based approach for the prediction of MCI-to-AD conversion with solely MRI data. Age correction and assisted structural brain image features can boost the prediction performance of CNN

Global Feature Pyramid Network

The visual feature pyramid has proven its effectiveness and efficiency in target detection tasks. Yet, current methodologies tend to overly emphasize inter-layer feature interaction, neglecting the crucial aspect of intra-layer feature adjustment. Experience underscores the significant advantages of intra-layer feature interaction in enhancing target detection tasks. While some approaches endeavor to learn condensed intra-layer feature representations using attention mechanisms or visual transformers, they overlook the incorporation of global information interaction. This oversight results in increased false detections and missed targets.To address this critical issue, this paper introduces the Global Feature Pyramid Network (GFPNet), an augmented version of PAFPN that integrates global information for enhanced target detection. Specifically, we leverage a lightweight MLP to capture global feature information, utilize the VNC encoder to process these features, and employ a parallel learnable mechanism to extract intra-layer features from the input image. Building on this foundation, we retain the PAFPN method to facilitate inter-layer feature interaction, extracting rich feature details across various levels.Compared to conventional feature pyramids, GFPN not only effectively focuses on inter-layer feature information but also captures global feature details, fostering intra-layer feature interaction and generating a more comprehensive and impactful feature representation. GFPN consistently demonstrates performance improvements over object detection baselines.Comment: dataset not ope