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Background The classification of cancer subtypes is of great importance to cancer disease diagnosis and therapy. Many supervised learning approaches have been applied to cancer subtype classification in the past few years, especially of deep learning based approaches. Recently, the deep forest model has been proposed as an alternative of deep neural networks to learn hyper-representations by using cascade ensemble decision trees. It has been proved that the deep forest model has competitive or even better performance than deep neural networks in some extent. However, the standard deep forest model may face overfitting and ensemble diversity challenges when dealing with small sample size and high-dimensional biology data. Results In this paper, we propose a deep learning model, so-called BCDForest, to address cancer subtype classification on small-scale biology datasets, which can be viewed as a modification of the standard deep forest model. The BCDForest distinguishes from the standard deep forest model with the following two main contributions: First, a named multi-class-grained scanning method is proposed to train multiple binary classifiers to encourage diversity of ensemble. Meanwhile, the fitting quality of each classifier is considered in representation learning. Second, we propose a boosting strategy to emphasize more important features in cascade forests, thus to propagate the benefits of discriminative features among cascade layers to improve the classification performance. Systematic comparison experiments on both microarray and RNA-Seq gene expression datasets demonstrate that our method consistently outperforms the state-of-the-art methods in application of cancer subtype classification. Conclusions The multi-class-grained scanning and boosting strategy in our model provide an effective solution to ease the overfitting challenge and improve the robustness of deep forest model working on small-scale data. Our model provides a useful approach to the classification of cancer subtypes by using deep learning on high-dimensional and small-scale biology data.

The Black–Scholes formula is an important formula for pricing a contingent claim in complete financial markets. This formula can be obtained under the assumption that the investor’s strategy is carried out according to a self-financing criterion; hence, there arise a set of self-financing portfolios corresponding to different contingent claims. The natural questions are: If an investor invests according to self-financing portfolios in the financial market, what are the maximal and minimal distributions of the investor’s wealth on some specific interval at the terminal time? Furthermore, if such distributions exist, how can the corresponding optimal portfolios be constructed? The present study applies the theory of backward stochastic differential equations in order to obtain an affirmative answer to the above questions. That is, the explicit formulations for the maximal and minimal distributions of wealth when adopting self-financing strategies would be derived, and the corresponding optimal (self-financing) portfolios would be constructed. Furthermore, this would verify the benefits of diversified portfolios in financial markets: that is, do not put all your eggs in the same basket.

Manipulation C-C coupling pathway is of great importance for selective CO 2 electroreduction but remain challenging. Herein, two model Cu-based catalysts, by modifying Cu nanowires with Ag nanoparticles (AgCu NW) and Ag single atoms (Ag 1 Cu NW), respectively, are rationally designed for exploring the C-C coupling mechanisms in electrochemical CO 2 reduction reaction (CO 2 RR). Compared to AgCu NW, the Ag 1 Cu NW exhibits a more than 10-fold increase of C 2 selectivity in CO 2 reduction to ethanol, with ethanol-to-ethylene ratio increased from 0.41 over AgCu NW to 4.26 over Ag 1 Cu NW. Via a variety of o perando /in-situ techniques and theoretical calculation, the enhanced ethanol selectivity over Ag 1 Cu NW is attributed to the promoted H 2 O dissociation over the atomically dispersed Ag sites, which effectively accelerated *CO hydrogenation to form *CHO intermediate and facilitated asymmetric *CO-*CHO coupling over paired Cu atoms adjacent to single Ag atoms. Results of this work provide deep insight into the C-C coupling pathways towards target C 2+ product and shed light on the rational design of efficient CO 2 RR catalysts with paired active sites. Manipulating the carbon-carbon coupling pathway in CO 2 electroreduction is vital yet challenging. Here, by studying two model copper-based catalysts with distinct ethylene and ethanol selectivity, authors investigate the mechanistic origins for symmetric and asymmetric carbon-carbon coupling.

BackgroundRhubarb is an important traditional Chinese medicine, and rhein is one of its most important active ingredients. Studies have found that rhein can improve ulcerative colitis by regulating gut microbes, but there are few reports on its effects on liver diseases. Therefore, this study aims to investigate these effects and underlying mechanisms.MethodsMice were given rhein (100 mg/kg), with both a normal control group and a model group receiving the same amount of normal saline for one week. Acute liver injury was induced in mice by intraperitoneal injection of D-GalN (800 mg/kg)/LPS (10 ug/kg). Samples (blood, liver, and stool) were then collected and assessed for histological lesions and used for 16S rRNA gene sequencing, high-performance liquid chromatography-mass spectrometry (LC-MS) and RNA-seq analysis.ResultsThe levels of ALT and AST in the Model group were abnormal higher compared to the normal control group, and the levels of ALT and AST were significantly relieved in the rhein group. Hepatic HE staining showed that the degree of liver injury in the rhein group was lighter than that in the model group, and microbiological results showed that norank_o:Clostridia_UCG-014, Lachnoclostridium, and Roseburia were more abundant in the model group compared to the normal control group. Notably, the rhein treatment group showed reshaped disturbance of intestinal microbial community by D-GalN/LPS and these mice also had higher levels of Verrucomicrobia, Akkermansiaceae and Bacteroidetes. Additionally, There were multiple metabolites that were significantly different between the normal control group and the model group, such as L-α-amino acid, ofloxacin-N-oxide, 1-hydroxy-1,3-diphenylpropan-2-one,and L-4-hydroxyglutamate semialdehyde, but that returned to normal levels after rhein treatment. The gene expression level in the model group also changed significantly, various genes such as Cxcl2, S100a9, Tnf, Ereg, and IL-10 were up-regulated, while Mfsd2a and Bhlhe41 were down-regulated, which were recovered after rhein treatment.ConclusionOverall, our results show that rhein alleviated D-GalN/LPS-induced acute liver injury in mice. It may help modulate gut microbiota in mice, thereby changing metabolism in the intestine. Meanwhile, rhein also may help regulate genes expression level to alleviate D-GalN/LPS-induced acute liver injury.

CYP 3A4 and CYP 3A5 are two important members of the human cytochrome P450 family. Although their overall structures are similar, the local structures of the active site are different, which directly leads to obvious individual differences in drug metabolic efficacy and toxicity. In this work, midazolam (MDZ) was selected as the probe substrate, and its interaction with two proteins, CYP 3A4 and CYP 3A5, was studied by molecular dynamics simulation (MD) along with the calculation of the binding free energy. The results show that two protein–substrate complexes have some similarities in enzyme–substrate binding; that is, in both complexes, Ser119 forms a high occupancy hydrogen bond with MDZ, which plays a key role in the stability of the interaction between MDZ and the enzymes. However, the complex formed by CYP 3A4 and MDZ is more stable, which may be attributed to the sandwich structure formed by the fluorophenyl group of the substrate with Leu216 and Leu482. Our study interprets the binding differences between two isoform–substrate complexes and reveals a structure–function relationship from the atomic perspective, which is expected to provide a theoretical basis for accurately measuring the effectiveness and toxicity of drugs for individuals in the era of precision medicine.

Avian influenza viral ribonucleoproteins (vRNPs) complete genome transcription and replication by interacting with host proteins, and RNA-dependent RNA polymerase (RdRp) is its major component. PB2 is a component of RdRp and plays an important role in viral RNA synthesis. Our previous mass spectrometry analysis identified PB2 interacted with avian cellular heterogeneous nuclear ribonucleoprotein AB (hnRNP AB). However, the specific mechanism of this interaction regulating viral replication needs to be further clarified. In this study, we found that avian hnRNP AB inhibited the replication of multiple subtypes of avian influenza viruses (AIVs) from different reservoirs, and the glycine-rich domain (GRD) of hnRNP AB was the function domain that inhibited AIV replication. Moreover, we demonstrated that the GRD of avian hnRNP AB interacted with the C-terminus of PB2, reducing the binding of PB1 to PB2 and interfering with RdRp assembly. Based on the previous discovery that hnRNP AB affected the nucleoplasmic distribution of PB2 mRNA, we have further explored the mechanism here. Mechanically, hnRNP AB intervened in the nuclear export of PB2 mRNA by reducing the binding ability of UAP56, and decreased PB2 expression to interfere with RdRp formation and reduce vRNA synthesis, which in turn inhibited viral replication. Collectively, this study demonstrated that the avian host protein hnRNP AB inhibited AIV replication by blocking assembly of RdRp and vRNA synthesis, in which was associated with UAP56-mediated nuclear export of PB2 mRNA, providing a potential target for antiviral intervention. Graphical Abstract

Background Genome-wide association studies (GWAS) that link genotype to phenotype represent an effective means to associate an individual genetic background with a disease or trait. However, single-omics data only provide limited information on biological mechanisms, and it is necessary to improve the accuracy for predicting the biological association between genotype and phenotype by integrating multi-omics data. Typically, gene expression data are integrated to analyze the effect of single nucleotide polymorphisms (SNPs) on phenotype. Such multi-omics data integration mainly follows two approaches: multi-staged analysis and meta-dimensional analysis, which respectively ignore intra-omics and inter-omics associations. Moreover, both approaches require omics data from a single sample set, and the large feature set of SNPs necessitates a large sample size for model establishment, but it is difficult to obtain multi-omics data from a single, large sample set. Results To address this problem, we propose a method of genotype-phenotype association based on multi-omics data from small samples. The workflow of this method includes clustering genes using a protein-protein interaction network and gene expression data, screening gene clusters with group lasso, obtaining SNP clusters corresponding to the selected gene clusters through expression quantitative trait locus data, integrating SNP clusters and corresponding gene clusters and phenotypes into three-layer network blocks, analyzing and predicting based on each block, and obtaining the final prediction by taking the average. Conclusions We compare this method to others using two datasets and find that our method shows better results in both cases. Our method can effectively solve the prediction problem in multi-omics data of small sample, and provide valuable resources for further studies on the fusion of more omics data.

Metal dispersion is a key concept in heterogeneous catalysis. The conventional approaches for its estimation strongly rely on chemisorption with different probe molecules. Albeit they can generally provide an ‘averaged’ value in a cost‐effective manner, the inhomogeneity of the metal species and the complicated metal‐support interactions pose formidable challenges for the accurate determination. Full metal species quantification (FMSQ) is introduced as an advanced method to depict the whole distribution of the metal species, ranging from single atoms to clusters and nanoparticles, in a practical solid catalyst. In this approach, automated analysis of massive high‐angle annular dark field scanning transmission electron microscopic images is realized through algorithms specialized in combining the electron microscopy‐based atom recognition statistics and deep learning‐driven nanoparticle segmentation. In this Concept article, different techniques for determining the metal dispersion are discussed with their pros and cons. FMSQ is highlighted for it can circumvent the drawbacks of conventional approaches, allowing more reliable structure‐performance relationships beyond the metal size. Full metal species quantification (FMSQ) based on massive high‐angle annular dark field scanning transmission electron microscopic images can provide detailed structural information of practical metal‐supported heterogeneous catalysts, allowing more reliable structure‐performance relationships regarding the metal size in comparison to the conventional techniques.

A highly sensitive temperature sensor based on surface plasmon resonance (SPR) of a side-polished single mode fiber is demonstrated. The sensor consists of a gold film coated side-polished fiber covered by a layer of UV-curable adhesive. Before introducing the UV-curable adhesive, the gold-coated fiber exhibits refractive index (RI) sensitivity of 1691.6 nm/RIU to 8800 nm/RIU in the range of 1.32 to 1.43. The resonant wavelength of the SPR sensor shifts to 650 nm when the adhesive is coated on the gold film, and is fixed at about 725 nm when the adhesive is cured. Due to the high thermo-optic and thermal expansion coefficient of the adhesive, the sensor structure achieves a temperature sensitivity of −0.978 nm/°C between 25 °C and 100 °C. The proposed optical fiber SPR sensor is simple, highly sensitive and cost effective, which may find potential applications for temperature measurements in the biomedical and environmental industries.

Background Sheath blight is an important disease caused by Rhizoctonia cerealis that affects wheat yields worldwide. No wheat varieties have been identified with high resistance or immunity to sheath blight. Understanding the sheath blight resistance mechanism is essential for controlling this disease. In this study, we investigated the response of wheat to Rhizoctonia cerealis infection by analyzing the cytological changes and transcriptomes of common wheat 7182 with moderate sensitivity to sheath blight and H83 with moderate resistance. Results The cytological observation showed that the growth of Rhizoctonia cerealis on the surface and its expansion inside the leaf sheath tissue were more rapid in the susceptible material. According to the transcriptome sequencing results, a total of 88685 genes were identified in both materials, including 20156 differentially expressed genes (DEGs) of which 12087 was upregulated genes and 8069 was downregulated genes. At 36 h post-inoculation, compared with the uninfected control, 11498 DEGs were identified in resistant materials, with 5064 downregulated genes and 6434 upregulated genes, and 13058 genes were detected in susceptible materials, with 6759 downregulated genes and 6299 upregulated genes. At 72 h post-inoculation, compared with the uninfected control, 6578 DEGs were detected in resistant materials, with 2991 downregulated genes and 3587 upregulated genes, and 7324 genes were detected in susceptible materials, with 4119 downregulated genes and 3205 upregulated genes. Functional annotation and enrichment analysis showed that the main pathways enriched for the DEGs included biosynthesis of secondary metabolites, carbon metabolism, plant hormone signal transduction, and plant–pathogen interaction. In particular, phenylpropane biosynthesis pathway is specifically activated in resistant variety H83 after infection. Many DEGs also belonged to the MYB, AP2, NAC, and WRKY transcription factor families. Conclusions Thus, we suggest that the normal functioning of plant signaling pathways and differences in the expression of key genes and transcription factors in some important metabolic pathways may be important for defending wheat against sheath blight. These findings may facilitate further exploration of the sheath blight resistance mechanism in wheat and the cloning of related genes.