Harnessing TME depicted by histological images to improve cancer prognosis through a deep learning system.

Spatial transcriptomics (ST) provides insights into the tumor microenvironment (TME), which is closely associated with cancer prognosis, but ST has limited clinical availability. In this study, we provide a powerful deep learning system to augment TME information based on histological images for patients without ST data, thereby empowering precise cancer prognosis. The system provides two connections to bridge existing gaps. The first is the integrated graph and image deep learning (IGI-DL) model, which predicts ST expression based on histological images with a 0.171 increase in mean correlation across three cancer types compared with five existing methods. The second connection is the cancer prognosis prediction model, based on TME depicted by spatial gene expression. Our survival model, using graphs with predicted ST features, achieves superior accuracy with a concordance index of 0.747 and 0.725 for The Cancer Genome Atlas breast cancer and colorectal cancer cohorts, outperforming other survival models. For the external Molecular and Cellular Oncology colorectal cancer cohort, our survival model maintains a stable advantage.

Bayesian network-based Mendelian randomization for variant prioritization and phenotypic causal inference.

Mendelian randomization is a powerful method for inferring causal relationships. However, obtaining suitable genetic instrumental variables is often challenging due to gene interaction, linkage, and pleiotropy. We propose Bayesian network-based Mendelian randomization (BNMR), a Bayesian causal learning and inference framework using individual-level data. BNMR employs the random graph forest, an ensemble Bayesian network structural learning process, to prioritize candidate genetic variants and select appropriate instrumental variables, and then obtains a pleiotropy-robust estimate by incorporating a shrinkage prior in the Bayesian framework. Simulations demonstrate BNMR can efficiently reduce the false-positive discoveries in variant selection, and outperforms existing MR methods in terms of accuracy and statistical power in effect estimation. With application to the UK Biobank, BNMR exhibits its capacity in handling modern genomic data, and reveals the causal relationships from hematological traits to blood pressures and psychiatric disorders. Its effectiveness in handling complex genetic structures and modern genomic data highlights the potential to facilitate real-world evidence studies, making it a promising tool for advancing our understanding of causal mechanisms.

Improving the accuracy of the SRK/T formula in Chinese with implanting less than 10 D IOL calculated by the SRK/T formula: the SRK/T-Li formula.

PURPOSE: To explore the accuracy of the improved SRK/T-Li formula in eyes following implantation of intraocular lens (IOL) of less than 10 D as calculated by using the SRK/T formula in Chinese. METHODS: A total of 489 eyes from 489 patients with cataracts were included in this study. These patients were divided into a training set (271 patients) and a testing set (218 patients). The IOL power calculated by using SRK/T was less than 10 D. We evaluated the accuracy of the modified SRK/T-Li formula (P = PSRK/T × 0.8 + 2 (P = implanted IOL power; PSRK/T = IOL power calculated by SRK/T)). We evaluated the mean absolute error (MAE), percentage of prediction error (PE) within ± 0.25, ± 0.50, and ± 1.00 D, and the percentage of postoperative hyperopia. RESULTS: The MAE values in order of lowest to highest were as follows: 0.412 D (SRK/T-Li), 0.414 D (Barrett Universal II, (BUII)), 0.814 D (SRK/T), and 1.039 D (Holladay 1). The percentage of PE within ± 0.25 D, ± 0.50 D, and ± 1.00 D was 38.99%, 69.27% and 92.66% (BUII), 40.83%, 69.27% and 94.04% (SRK/T-Li), 20.64%, 41.28% and 71.56% (SRK/T), and 7.34%, 16.51% and 53.21% (Holladay 1), respectively. SRK/T-Li had the smallest postoperative hyperopic shift. CONCLUSIONS: For Chinese patients with an IOL power of less than 10 D as calculated by using the SRK/T, the SRK/T-Li has good accuracy and is the best choice to reduce postoperative hyperopic shift.

A news-based climate policy uncertainty index for China.

Climate policies can have a significant impact on the economy. However, these policies have often been associated with uncertainty. Quantitative assessment of the socioeconomic impact of climate policy uncertainty is equally or perhaps more important than looking at the policies themselves. Using a deep learning algorithm-the MacBERT model-this study constructed indices of Chinese climate policy uncertainty (CCPU) at the national, provincial and city levels for the first time. The CCPU indices are based on the text mining of news published by a set of major newspapers in China. A clear upward trend was found in the indices, demonstrating increasing policy uncertainties in China in addressing climate change. There is also evidence of clear regional heterogeneity in subnational indices. The CCPU dataset can provide a useful source of information for government actors, academics and investors in understanding the dynamics of climate policies in China. These indices can also be used to investigate the empirical relationship between climate policy uncertainty and other socioeconomic factors in China.

Enhanced Energy Harvesting Performance of Triboelectric Nanogenerators via Dielectric Property Regulation.

Triboelectric nanogenerators (TENGs) hold great application prospects and research values in the field of energy harvest. The friction layer of TENGs plays an important impact role in their output performance. Therefore, composition modulation of the friction layer is of great significance. In this paper, the xMWCNT/CS composite films with multiwalled carbon nanotubes (MWCNTs) as a filler and chitosan (CS) as a matrix were prepared and a TENG based on the xMWCNT/CS composite films (xMWCNT/CS-TENG) was constructed. The introduction of the conductive filler MWCNT significantly improves the dielectric constant of the films due to the Maxwell-Wagner relaxation. As a result, the output performance of the xMWCNT/CS-TENG is greatly enhanced. The best values of an open-circuit voltage of 85.8 V, a short-circuit current of 8.7 µA, and a transfer charge of 29 nC were obtained in the TENG with an optimum MWCNT content of x = 0.8 wt % under an external force of 50 N and a frequency of 2 Hz. The TENG can sensitively perceive human activities such as walking. Our results evidence that the xMWCNT/CS-TENG is a flexible, wearable, and eco-friendly energy collector, holding great prospects in health care and body information monitoring.

A high-efficiency differential expression method for cancer heterogeneity using large-scale single-cell RNA-sequencing data.

Colorectal cancer is a highly heterogeneous disease. Tumor heterogeneity limits the efficacy of cancer treatment. Single-cell RNA-sequencing technology (scRNA-seq) is a powerful tool for studying cancer heterogeneity at cellular resolution. The sparsity, heterogeneous diversity, and fast-growing scale of scRNA-seq data pose challenges to the flexibility, accuracy, and computing efficiency of the differential expression (DE) methods. We proposed HEART (high-efficiency and robust test), a statistical combination test that can detect DE genes with various sources of differences beyond mean expression changes. To validate the performance of HEART, we compared HEART and the other six popular DE methods on various simulation datasets with different settings by two simulation data generation mechanisms. HEART had high accuracy ( F 1 score >0.75) and brilliant computational efficiency (less than 2 min) on multiple simulation datasets in various experimental settings. HEART performed well on DE genes detection for the PBMC68K dataset quantified by UMI counts and the human brain single-cell dataset quantified by read counts ( F 1 score = 0.79, 0.65). By applying HEART to the single-cell dataset of a colorectal cancer patient, we found several potential blood-based biomarkers (CTTN, S100A4, S100A6, UBA52, FAU, and VIM) associated with colorectal cancer metastasis and validated them on additional spatial transcriptomic data of other colorectal cancer patients.

STS-BN: An efficient Bayesian network method for detecting causal SNPs.

Background: The identification of the causal SNPs of complex diseases in large-scale genome-wide association analysis is beneficial to the studies of pathogenesis, prevention, diagnosis and treatment of these diseases. However, existing applicable methods for large-scale data suffer from low accuracy. Developing powerful and accurate methods for detecting SNPs associated with complex diseases is highly desired. Results: We propose a score-based two-stage Bayesian network method to identify causal SNPs of complex diseases for case-control designs. This method combines the ideas of constraint-based methods and score-and-search methods to learn the structure of the disease-centered local Bayesian network. Simulation experiments are conducted to compare this new algorithm with several common methods that can achieve the same function. The results show that our method improves the accuracy and stability compared to several common methods. Our method based on Bayesian network theory results in lower false-positive rates when all correct loci are detected. Besides, real-world data application suggests that our algorithm has good performance when handling genome-wide association data. Conclusion: The proposed method is designed to identify the SNPs related to complex diseases, and is more accurate than other methods which can also be adapted to large-scale genome-wide analysis studies data.

GapClust is a light-weight approach distinguishing rare cells from voluminous single cell expression profiles.

Single cell RNA sequencing (scRNA-seq) is a powerful tool in detailing the cellular landscape within complex tissues. Large-scale single cell transcriptomics provide both opportunities and challenges for identifying rare cells playing crucial roles in development and disease. Here, we develop GapClust, a light-weight algorithm to detect rare cell types from ultra-large scRNA-seq datasets with state-of-the-art speed and memory efficiency. Benchmarking on diverse experimental datasets demonstrates the superior performance of GapClust compared to other recently proposed methods. When applying our algorithm to an intestine and 68 k PBMC datasets, GapClust identifies the tuft cells and a previously unrecognised subtype of monocyte, respectively.

High-Dimensional Mediation Analysis With Confounders in Survival Models.

Mediation analysis is a common statistical method for investigating the mechanism of environmental exposures on health outcomes. Previous studies have extended mediation models with a single mediator to high-dimensional mediators selection. It is often assumed that there are no confounders that influence the relations among the exposure, mediator, and outcome. This is not realistic for the observational studies. To accommodate the potential confounders, we propose a concise and efficient high-dimensional mediation analysis procedure using the propensity score for adjustment. Results from simulation studies demonstrate the proposed procedure has good performance in mediator selection and effect estimation compared with methods that ignore all confounders. Of note, as the sample size increases, the performance of variable selection and mediation effect estimation is as well as the results shown in the method which include all confounders as covariates in the mediation model. By applying this procedure to a TCGA lung cancer data set, we find that lung cancer patients who had serious smoking history have increased the risk of death via the methylation markers cg21926276 and cg20707991 with significant hazard ratios of 1.2093 (95% CI: 1.2019-1.2167) and 1.1388 (95% CI: 1.1339-1.1438), respectively.

Extreme risk spillover between chinese and global crude oil futures.

This paper investigates the risk spillover between China's crude oil futures and international crude oil futures by constructing upside and downside VaR connectedness networks. The findings show that China's crude oil futures behave as a net risk receiver in the global crude oil system, in which Brent and WTI play the leading roles in risk transmission in the system. The dynamic results indicate that the risk spillover between Chinese and international crude oil futures presents obvious time-varying characteristics and has risen sharply since the beginning of 2020, induced by the COVID-19 pandemic.

OsIRO3 Plays an Essential Role in Iron Deficiency Responses and Regulates Iron Homeostasis in Rice.

Iron (Fe) homeostasis is essential for plant growth and development, and it is strictly regulated by a group of transcriptional factors. Iron-related transcription factor 3 (OsIRO3) was previously identified as a negative regulator for Fe deficiency response in rice. However, the molecular mechanisms by which OsIRO3 regulate Fe homeostasis is unclear. Here, we report that OsIRO3 is essential for responding to Fe deficiency and maintaining Fe homeostasis in rice. OsIRO3 is expressed in the roots, leaves, and base nodes, with a higher level in leaf blades at the vegetative growth stage. Knockout of OsIRO3 resulted in a hypersensitivity to Fe deficiency, with severe necrosis on young leaves and defective root development. The iro3 mutants accumulated higher levels of Fe in the shoot under Fe-deficient conditions, associated with upregulating the expression of OsNAS3, which lead to increased accumulation of nicotianamine (NA) in the roots. Further analysis indicated that OsIRO3 can directly bind to the E-box in the promoter of OsNAS3. Moreover, the expression of typical Fe-related genes was significantly up-regulated in iro3 mutants under Fe-sufficient conditions. Thus, we conclude that OsIRO3 plays a key role in responding to Fe deficiency and regulates NA levels by directly, negatively regulating the OsNAS3 expression.

Effects of heat treatment, homogenization pressure, and overprocessing on the content of furfural compounds in liquid milk.

BACKGROUND: Sterilization of milk is aimed at killing the microorganisms present. There are three main sterilization methods commonly used in milk processing: high temperature and short time (HTST) pasteurization, ultrahigh pasteurization (UP), and ultrahigh temperature (UHT) sterilization. The Maillard reaction is of special interest in studying the effect of heat treatment on milk quality. Furfural compounds are one of the typical intermediates of the Maillard reaction, which have safety risks related to mutagenic and genotoxic effects. The furfural compounds content is directly related to the heat treatment intensity. RESULTS: The furfural compounds content was measured using high-performance liquid chromatography with ultraviolet detection in 12 min. Then, 13 levels of heat treatment intensity (combinations of temperature and time) and three levels of homogenization pressure were selected to study the change of the furfural compounds content after different processing technologies in a pilot plant. The results show a higher temperature treatment can stimulate more Maillard reaction intermediates, such as hydroxymethylfurfural and furfural. A temperature regression evaluation model and content prediction models of hydroxymethylfurfural and furfural were developed to quantify the relationship between the furfural content and heat treatment with the data from the pilot plant. CONCLUSION: Based on the temperature evaluation model, the heating temperature of three milk products was evaluated. The homogenization pressures had little effect on the furfural content in liquid milk. The emergence of the furyl methyl ketone and methylfurfural can be detected after overprocessing of the liquid milk. © 2020 Society of Chemical Industry.

Association of Two Polymorphisms, rs1061170 and rs1410996, in Complement Factor H with Age-Related Macular Degeneration in an Asian Population: A Meta-Analysis.

BACKGROUND: With the increasing number of studies indicating that two single-nucleotide polymorphisms (SNPs), rs1061170 and rs1410996, in complement factor H (CFH) might be associated with the susceptibility to age-related macular degeneration (AMD), the exact association still remains uncertain. Thus, we conducted a meta-analysis to systematically summarize and clarify the association between the two SNPs and the AMD risk particularly in an Asian population. METHODS: A systematic search of studies on the association of two SNPs with the susceptibility to AMD was conducted in PubMed, Embase and Web of Science. Summary odds ratios (ORs) and 95% confidence intervals (CIs) of allele contrast and genotype contrast were estimated using the random or fixed effects model. The Q statistic test was used to identify heterogeneity, and the funnel plot was adopted to evaluate publication bias. A total of 19 case-control studies on rs1061170 and 8 studies on rs1410996 were included. RESULTS: Clearly a significantly increased trend of AMD was observed with the rs1061170 (T vs. C: OR = 1.91, 95% CI = 1.71-2.13, pH = 0.029; TC vs. CC: OR = 2.11, 95% CI = 1.30-3.42, pH = 0.792; TT vs. CC: OR = 3.90, 95% CI = 2.45-6.22, pH = 0.774). Similarly, the rs1410996 polymorphism also showed a rising AMD tendency (T vs. C: OR = 1.48, 95% CI = 1.17-1.87, pH < 0.001; TC vs. CC: OR = 1.52, 95% CI = 1.13-2.04, pH = 0.002; TT vs. CC: OR = 2.10, 95% CI = 1.27-3.49, pH < 0.001). What is more, subgroup analysis revealed that both polymorphisms indicated a high risk of nAMD (neovascular AMD) in Asian populations. CONCLUSIONS: This meta-analysis suggested that CFH rs1061170 and rs1410996 polymorphisms were associated with AMD risk, both of which demonstrated a higher susceptibility to AMD, especially to nAMD. However, the results of rs1410996 should be interpreted with caution due to the limited sample and heterogeneity. Large-scale and well-designed studies are needed to validate our findings.