TTMFN: Two-stream Transformer-based Multimodal Fusion Network for Survival Prediction

Survival prediction plays a crucial role in assisting clinicians with the development of cancer treatment protocols. Recent evidence shows that multimodal data can help in the diagnosis of cancer disease and improve survival prediction. Currently, deep learning-based approaches have experienced increasing success in survival prediction by integrating pathological images and gene expression data. However, most existing approaches overlook the intra-modality latent information and the complex inter-modality correlations. Furthermore, existing modalities do not fully exploit the immense representational capabilities of neural networks for feature aggregation and disregard the importance of relationships between features. Therefore, it is highly recommended to address these issues in order to enhance the prediction performance by proposing a novel deep learning-based method. We propose a novel framework named Two-stream Transformer-based Multimodal Fusion Network for survival prediction (TTMFN), which integrates pathological images and gene expression data. In TTMFN, we present a two-stream multimodal co-attention transformer module to take full advantage of the complex relationships between different modalities and the potential connections within the modalities. Additionally, we develop a multi-head attention pooling approach to effectively aggregate the feature representations of the two modalities. The experiment results on four datasets from The Cancer Genome Atlas demonstrate that TTMFN can achieve the best performance or competitive results compared to the state-of-the-art methods in predicting the overall survival of patients

Assessment of the Effects of Fencing Enclosure on Soil Quality Based on Minimum Data Set in Biru County of the Qinghai–Tibet Plateau, China

Fencing enclosures play an important role in improving ecological quality. There is a direct impact of implementing fencing enclosures on the change in soil quality. The soil quality index was used to examine the effects of fencing enclosures for different years (7 and 11 years) on soil quality in Biru County of Qinghai–Tibet Plateau, China. The fencing enclosure significantly increased soil water content, non-capillary porosity, soil organic matter, total nitrogen, total phosphorus, and alkali-hydrolyzable nitrogen, and significantly decreased the soil bulk density. The soil quality gradually improved as the fencing enclosure time length increased, probably due to the increase of vegetation coverage and biomass under the fencing enclosure. The minimum data set was composed of soil organic matter, capillary porosity, total potassium, and non-capillary porosity. The minimum data set was significantly correlated with the total data set and could replace the total data set for soil quality evaluation in the fencing enclosure project area. In summary, our study reflects that fencing enclosures significantly improve soil quality, and the implementation of the fencing enclosure project will effectively curb land degradation in Biru County of the Qinghai–Tibet Plateau, China

Valorization of Fish Processing By-Products: Microstructural, Rheological, Functional, and Properties of Silver Carp Skin Type I Collagen

The objective of this study was to develop aquatic collagen production from fish processing by-product skin as a possible alternative to terrestrial sources. Silver carp skin collagen (SCSC) was isolated and identified as type I collagen, and LC-MS/MS analysis confirmed the SCSC as Hypophthalmichthys molitrix type I collagen, where the yield of SCSC was 40.35 ± 0.63% (dry basis weight). The thermal denaturation temperature (Td) value of SCSC was 30.37 °C, which was superior to the collagen of deep-sea fish and freshwater fish. Notably, SCSC had higher thermal stability than human placental collagen, and the rheological experiments showed that the SCSC was a shear-thinning pseudoplastic fluid. Moreover, SCSC was functionally superior to some other collagens from terrestrial sources, such as sheep, chicken cartilage, and pig skin collagen. Additionally, SCSC could provide a suitable environment for MC3T3-E1 cell growth and maintain normal cellular morphology. These results indicated that SCSC could be used for further applications in food, cosmetics, and biomedical fields

Influencing factors and contribution analysis of CO2 emissions originating from final energy consumption in Sichuan Province, China

Within the context of CO2 emission peaking and carbon neutrality, the study of CO2 emissions at the provincial level is few. Sichuan Province in China has not only superior clean energy resources endowment but also great potential for the reduction of CO2 emissions. Therefore, using logarithmic mean Divisia index (LMDI) model to analysis the influence degree of different influencing factors on CO2 emissions from final energy consumption in Sichuan Province, so as to formulate corresponding emission reduction countermeasures from different paths according to the influencing factors. Based on the data of final energy consumption in Sichuan Province from 2010 to 2019, we calculated CO2 emission by the indirect emission calculation method. The influencing factors of CO2 emissions originating from final energy consumption in Sichuan Province were decomposed into population size, economic development, industrial structure, energy consumption intensity, and energy consumption structure by the Kaya–logarithmic mean Divisia index (LMDI) decomposition model. At the same time, grey correlation analysis was used to identify the correlation between CO2 emissions originating from final energy consumption and the influencing factors in Sichuan Province. The results showed that population size, economic development and energy consumption structure have positive contributions to CO2 emissions from final energy consumption in Sichuan Province, and economic development has a significant contribution to CO2 emissions from final energy consumption, with a contribution rate of 519.11%. The industrial structure and energy consumption intensity have negative contributions to CO2 emissions in Sichuan Province, and both of them have significant contributions, among which the contribution rate of energy consumption structure was 325.96%. From the perspective of industrial structure, secondary industry makes significant contributions and will maintain a restraining effect; from the perspective of energy consumption structure, industry sector has a significant contribution. The results of this paper are conducive to the implementation of carbon emission reduction policies in Sichuan Province

Human umbilical cord mesenchymal stem cell derived exosomes (HUCMSC-exos) recovery soluble fms-like tyrosine kinase-1 (sFlt-1)-induced endothelial dysfunction in preeclampsia

Abstract Background Preeclampsia is a unique multisystem disorder that affects 5–8% of pregnancies. A high level of soluble fms-like tyrosine kinase-1 (sFlt-1) is a hallmark of preeclampsia that causes endothelial dysfunction. Exosomes derived from mesenchymal stem cells (MSCs) have been indicated to improve endothelial performances by transporting signals to target cells. We hypothesized that exosomes derived from MSCs have potential effects against preeclampsia. Methods We collected human umbilical cord MSC-derived exosomes (HUCMSC-exos) by ultracentrifugation. The size and morphology of the exosomes were examined using a transmission electron microscope and nanoparticle tracking analysis. Pregnant mice were injected with murine sFlt-1 adenovirus to build the preeclampsia-like mouse model and then treated with HUCMSC-exos. Human umbilical vein endothelial cells (HUVECs) were infected with lentiviruses expressing tet-on-sFlt-1 to obtain cells overexpressing sFlt-1. Cell proliferation and migration assays were used to measure the endothelial functions. The exosomes enriched proteins underlying mechanisms were explored by proteomic analysis. Results In the current study, we successfully collected the cup-shaped HUCMSC-exos with diameters of 30–150 nm. In the sFlt-1-induced preeclampsia mouse model, HUCMSC-exos exhibited beneficial effects on adverse birth events by decreasing blood pressure and improving fetal birth weight. In addition, preeclamptic dams that were injected with HUCMSC-exos had rebuilt dense placental vascular networks. Furthermore, we observed that HUCMSC-exos partially rescued sFlt-1-induced HUVECs dysfunction in vitro. Proteomics analysis of HUCMSC-exos displayed functional enrichment in biological processes related to vesicle-mediated transport, cell communication, cell migration, and angiogenesis. Conclusion We propose that exosomes derived from HUCMSCs contain abundant Versican and play beneficial roles in the birth outcomes of sFlt-1-induced preeclamptic mice by promoting angiogenesis. Graphical Abstrac

Novel deep learning-based transcriptome data analysis for drug-drug interaction prediction with an application in diabetes

Abstract Background Drug-drug interaction (DDI) is a serious public health issue. The L1000 database of the LINCS project has collected millions of genome-wide expressions induced by 20,000 small molecular compounds on 72 cell lines. Whether this unified and comprehensive transcriptome data resource can be used to build a better DDI prediction model is still unclear. Therefore, we developed and validated a novel deep learning model for predicting DDI using 89,970 known DDIs extracted from the DrugBank database (version 5.1.4). Results The proposed model consists of a graph convolutional autoencoder network (GCAN) for embedding drug-induced transcriptome data from the L1000 database of the LINCS project; and a long short-term memory (LSTM) for DDI prediction. Comparative evaluation of various machine learning methods demonstrated the superior performance of our proposed model for DDI prediction. Many of our predicted DDIs were revealed in the latest DrugBank database (version 5.1.7). In the case study, we predicted drugs interacting with sulfonylureas to cause hypoglycemia and drugs interacting with metformin to cause lactic acidosis, and showed both to induce effects on the proteins involved in the metabolic mechanism in vivo. Conclusions The proposed deep learning model can accelerate the discovery of new DDIs. It can support future clinical research for safer and more effective drug co-prescription