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Abstract Background Predictive, preventive, and personalized medicine (PPPM/3PM) is a strategy aimed at improving the prognosis of cancer, and programmed cell death (PCD) is increasingly recognized as a potential target in cancer therapy and prognosis. However, a PCD-based predictive model for serous ovarian carcinoma (SOC) is lacking. In the present study, we aimed to establish a cell death index (CDI)–based model using PCD-related genes. Methods We included 1254 genes from 12 PCD patterns in our analysis. Differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were screened. Subsequently, 14 PCD-related genes were included in the PCD-gene-based CDI model. Genomics, single-cell transcriptomes, bulk transcriptomes, spatial transcriptomes, and clinical information from TCGA-OV, GSE26193, GSE63885, and GSE140082 were collected and analyzed to verify the prediction model. Results The CDI was recognized as an independent prognostic risk factor for patients with SOC. Patients with SOC and a high CDI had lower survival rates and poorer prognoses than those with a low CDI. Specific clinical parameters and the CDI were combined to establish a nomogram that accurately assessed patient survival. We used the PCD-genes model to observe differences between high and low CDI groups. The results showed that patients with SOC and a high CDI showed immunosuppression and hardly benefited from immunotherapy; therefore, trametinib_1372 and BMS-754807 may be potential therapeutic agents for these patients. Conclusions The CDI-based model, which was established using 14 PCD-related genes, accurately predicted the tumor microenvironment, immunotherapy response, and drug sensitivity of patients with SOC. Thus this model may help improve the diagnostic and therapeutic efficacy of PPPM.

Tandem aquaculture mode is an innovative and burgeoning approach in aquaculture, designed to enhance aquatic biological production while achieving zero discharge of aquaculture effluent. By optimizing aquaculture density, this mode can fully exploit its potential advantages. In this study, we constructed tandem culture ponds (a shrimp pond with an area of 8000 m2 and a razor clam pond with an area of 4000 m2) in the coastal area, and the polyculture pond (with an area of 2600 m2) was used as the control. The experiment lasted a total of 317 days (including 32 days of shrimp culture), and initial stocking densities (razor clams: 487 ind/m2, shrimp: 40 ind/m2) and sizes (razor clams: 0.19 ± 0.05 g, shrimp: 0.38 ± 0.08 g) remained consistent in both modes. Through monthly sampling, we estimated the carrying capacity of Sinonovacula constricta by Parsons T R & Takahashi M Trophic Efficiency Method and Tait coastal waters energy flow analysis model. The carrying capacity of Litopenaeus vannamei was estimated by using models based on non-ionic ammonia emission standard and nitrogen emission standard. Results show that the carrying capacity of S. constricta estimated by the two models was 364 ind/m2 and 427 ind/m2 in the tandem culture pond, and the carrying capacity of L. vannamei estimated based on the two emission standards was 73 ind/m2 and 64 ind/m2. At the same time, it was estimated that the carrying capacity of S. constricta in the traditional polyculture pond was 88 ind/m2 and 103 ind/m2 respectively, and that of L. vannamei was 42 ind/m2 and 31 ind/m2 respectively. Converting the model results to yields, we found that both razor clam and shrimp yields were significantly higher (p

Background SLE is a complex autoimmune disease with heterogeneous manifestations and unpredictable outcomes. Early diagnosis is challenging due to non-specific symptoms, and current treatments only manage symptoms. Epigenetic alternations, including 5-Hydroxymethylome (5hmC) modifications, are important contributors to SLE pathogenesis. However, the 5hmC modification status in circulating cell-free DNA (cfDNA) of patients with SLE remains largely unexplored. We investigated the distribution of 5hmC in cfDNA of patients with SLE and healthy controls (HCs), and explored its potential as an SLE diagnosis marker.Methods We used 5hmC-Seal to generate genome-wide 5hmC profiles of plasma cfDNA and bioinformatics analysis to screen differentially hydroxymethylated regions (DhMRs). In vitro mechanistic exploration was conducted to investigate the regulatory effect of CCCTC-binding factor (CTCF) in 5hmC candidate biomarkers.Results We found distinct differences in genomic regions and 5hmC modification motif patterns between patients with SLE and HCs, varying with disease progression. Increased 5hmC modification enrichment was detected in SLE. Additionally, we screened 151 genes with hyper-5hmC, which are significantly involved in SLE-related processes, and 5hmC-modified BCL2, CD83, ETS1 and GZMB as SLE biomarkers. Our findings suggest that CTCF regulates 5hmC modification of these genes by recruiting TET (ten-eleven translocation) protein, and CTCF knockdown affected the protein expression of these genes in vitro.Conclusions Our findings demonstrate the increased 5hmC distribution in plasma cfDNA in different disease activity in patients with SLE compared with HCs and relating DhMRs involved in SLE-associated pathways. Furthermore, we identified a panel of SLE relevant biomarkers, and these viewpoints could provide insight into the pathogenesis of SLE.

The abundance and biological contribution of natural killer (NK) cells in cancer are controversial. Here, we aim to uncover clinical relevance and cellular roles of NK cells in colon cancer liver metastasis (CCLM). Here, we integrated single-cell RNA-sequencing, spatial transcriptomics (ST), and bulk RNA-sequencing datasets to investigate NK cells’ biological properties and functions in the microenvironment of primary and liver metastatic tumors. Results were validated through an in vitro co-culture experiment based on bioinformatics analysis. Useing single-cell RNA-sequencing and ST, we mapped the immune cellular landscape of colon cancer and well-matched liver metastatic cancer. We discovered that GZMK+ resting NK cells increased significantly in tumor tissues and were enriched in the tumor regions of both diseases. After combining bulk RNA and clinical data, we observed that these NK cell subsets contributed to a worse prognosis. Meanwhile, KIR2DL4+ activated NK cells exhibited the opposite position and relevance. Pseudotime cell trajectory analysis revealed the evolution of activated to resting NK cells. In vitro experiments further confirmed that tumor-cell-co-cultured NK cells exhibited a decidual-like status, as evidenced by remarkable increasing CD9 expression. Functional experiments finally revealed that NK cells exhibited tumor-activating characteristics by promoting the dissociation of SCF (stem cell factor) on the tumor cells membrane depending on cell-to-cell interaction, as the supernatant of the co-culture system enhanced tumor progression. In summary, our findings revealed resting NK cells exhibited a clinical relevance with CCLM, which may be exploited for novel strategies to improve therapeutic outcomes for patients with CCLM.

The extent of removal of lignin and hemicellulose are crucial indicators for evaluating the efficiency of enzymatic hydrolysis of crop straw. Numerous factors influence these two indices. Establishing a quantitative model that correlates these factors with hydrolysis efficiency is essential, as it can guide efficient hydrolysis. In this study, a predictive method for enzymatic hydrolysis efficiency in crop straw was proposed using Grey relational analysis (GRA), Kernel principal component analysis (KPCA), and a least squares support vector machine (LSSVM). The authors collected a dataset from actual production data and developed an efficiency predictive model using GRA for variable selection, KPCA for dimensionality reduction, and LSSVM for model training. This model allows for the direct estimation of the final enzymatic hydrolysis efficiency based on production condition variables, which can include enzyme amount, temperatures, pH, time, agitation, and straw dimensions. Extensive experimental testing validated the effectiveness of the proposed method, resulting in minimal errors, a high degree of fit, and exceptional performance. The methodology described in this study can serve as a foundation for optimising the design of efficient enzymatic hydrolysis production processes for crop straw. Additionally, it offers valuable soft measurements to support efficient control of the enzymatic hydrolysis process.

Computational color constancy (CCC) is to endow computers or cameras with the capability to remove the color bias effect caused by different scene illuminations. The first procedure of CCC is illuminant estimation, i.e., to calculate the illuminant color for a given image scene. Recently, some methods directly mapping image features to illuminant estimation provide an effective and robust solution for this issue. Nevertheless, due to diverse image features, it is uncertain to select which features to model illuminant color. In this research, a series of artificial features weaved into a mapping-based illuminant estimation framework is extensively investigated. This framework employs a multi-model structure and integrates the functions of kernel-based fuzzy c-means (KFCM) clustering, non-negative least square regression (NLSR), and fuzzy weighting. By comparing the resulting performance of different features, the features more correlated to illuminant estimation are found in the candidate feature set. Furthermore, the composite features are designed to achieve the outstanding performances of illuminant estimation. Extensive experiments are performed on typical benchmark datasets and the effectiveness of the proposed method has been validated. The proposed method makes illuminant estimation an explicit transformation of suitable image features with regressed and fuzzy weights, which has significant potential for both competing performances and fast implementation against state-of-the-art methods.

Computational color constancy (CCC) is a fundamental prerequisite for many computer vision tasks. The key of CCC is to estimate illuminant color so that the image of a scene under varying illumination can be normalized to an image under the canonical illumination. As a type of solution, combination algorithms generally try to reach better illuminant estimation by weighting other unitary algorithms for a given image. However, due to the diversity of image features, applying the same weighting combination strategy to different images might result in unsound illuminant estimation. To address this problem, this study provides an effective option. A two-step strategy is first employed to cluster the training images, then for each cluster, ANFIS (adaptive neuro-network fuzzy inference system) models are effectively trained to map image features to illuminant color. While giving a test image, the fuzzy weights measuring what degrees the image belonging to each cluster are calculated, thus a reliable illuminant estimation will be obtained by weighting all ANFIS predictions. The proposed method allows illuminant estimation to be dynamic combinations of initial illumination estimates from some unitary algorithms, relying on the powerful learning and reasoning capabilities of ANFIS. Extensive experiments on typical benchmark datasets demonstrate the effectiveness of the proposed approach. In addition, although there is an initial observation that some learning-based methods outperform even the most carefully designed and tested combinations of statistical and fuzzy inference systems, the proposed method is good practice for illuminant estimation considering fuzzy inference eases to implement in imaging signal processors with if-then rules and low computation efforts.

Reversible thermochromic paper able to resist counterfeiting was prepared by adding reversible thermochromic microcapsules (RTM) to a slurry of cellulosic fibers, a process that is difficult to imitate. However, the loss of RTM is one of the biggest problems that inhibits industrial use of this approach. So, the retention of RTM in pulp was investigated. The RTM was synthesized by in-situ polymerization, and its properties were characterized. It exhibited strong color contrast between cool and heated conditions, and such behavior could be used to achieve distinctive anticounterfeiting characteristics in the paper. The surface of each microcapsule was smooth, and there was no coherence between particles. The diameters of the microcapsules were mainly in the range 3.0 μm to 5.0 μm. Retention of RTM was closely related to beating degree and pulp composition; the higher the beating degree and hardwood pulp content, the higher the retention of RTM. On the other hand, the retention of RTM was influenced by filler and retention aid. Retention aid promoted retention of RTM to some degree; however, filler was not conductive to retention of RTM. Different addition sequences between RTM and filler or retention aid also influenced the retention of RTM.

The present study aimed to elucidate key molecular mechanisms in the progression of diffuse intrinsic pontine glioma (DIPG). The gene expression profile GSE50021, which consisted of 35 pediatric DIPG samples and 10 normal brain samples, was downloaded from the Gene Expression Omnibus database. The differentially-expressed genes (DEGs) in the pediatric DIPG samples were identified. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathways of DEGs were enriched and analyzed. The protein-protein interaction (PPI) network of the DEGs was constructed and functional modules of the PPI network were disclosed using ClusterONE. A total of 679 DEGs (454 up- and 225 downregulated) were identified in the pediatric DIPG samples. DEGs were significantly enriched in various GO terms, and KEGG and Reactome pathways. The PPI network of upregulated (153 nodes and 298 connections) and downregulated (71 nodes and 124 connections) DEGs, and two crucial modules, were obtained. Downregulated genes in module 2, such as cholecystokinin (CCK), gastrin (GAST), adenylate cyclase 2 (brain) (ADCY2) and 5-hydroxytryptamine (serotonin) receptor 7 (HTR7), were significantly enriched in the calcium signaling pathway, the neuroactive ligand-receptor interaction pathway and in GO terms, such as the G-protein coupled receptor (GPCR) signaling pathway, while upregulated genes in module 1 were not enriched in any pathways or GO terms. CCK and GAST associated with the GPCR signaling pathway, HTR7 enriched in the neuroactive ligand-receptor interaction, and ADCY2 and HTR7 involved in the calcium signaling pathway may be key mechanisms playing crucial roles in the development and progression of DIPG. [ABSTRACT FROM AUTHOR]

Reversible thermochromic paper able to resist counterfeiting was prepared by adding reversible thermochromic microcapsules (RTM) to a slurry of cellulosic fibers, a process that is difficult to imitate. However, the loss of RTM is one of the biggest problems that inhibits industrial use of this approach. So, the retention of RTM in pulp was investigated. The RTM was synthesized by in-situ polymerization, and its properties were characterized. It exhibited strong color contrast between cool and heated conditions, and such behavior could be used to achieve distinctive anticounterfeiting characteristics in the paper. The surface of each microcapsule was smooth, and there was no coherence between particles. The diameters of the microcapsules were mainly in the range 3.0 µm to 5.0 µm. Retention of RTM was closely related to beating degree and pulp composition; the higher the beating degree and hardwood pulp content, the higher the retention of RTM. On the other hand, the retention of RTM was influenced by filler and retention aid. Retention aid promoted retention of RTM to some degree; however, filler was not conductive to retention of RTM. Different addition sequences between RTM and filler or retention aid also influenced the retention of RTM. [ABSTRACT FROM AUTHOR]