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NRC publication: Yes

Esophageal cancer (EC) is a type of aggressive cancer without clinically relevant molecular subtypes, hindering the development of effective strategies for treatment. To define molecular subtypes of EC, we perform mass spectrometry-based proteomic and phosphoproteomics profiling of EC tumors and adjacent non-tumor tissues, revealing a catalog of proteins and phosphosites that are dysregulated in ECs. The EC cohort is stratified into two molecular subtypes—S1 and S2—based on proteomic analysis, with the S2 subtype characterized by the upregulation of spliceosomal and ribosomal proteins, and being more aggressive. Moreover, we identify a subtype signature composed of ELOA and SCAF4, and construct a subtype diagnostic and prognostic model. Potential drugs are predicted for treating patients of S2 subtype, and three candidate drugs are validated to inhibit EC. Taken together, our proteomic analysis define molecular subtypes of EC, thus providing a potential therapeutic outlook for improving disease outcomes in patients with EC. Proteomics can aid in the identification of molecular subtypes in cancers. Here, the authors perform proteomic profiling of 124 paired oesophageal cancer and adjacent non-tumour tissues and identify two subtypes that are associated with patient survival for therapeutic targeting.

Animal studies implicate meningeal lymphatic dysfunction in the pathogenesis of neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease (PD). However, there is no direct evidence in humans to support this role 1 – 5 . In this study, we used dynamic contrast-enhanced magnetic resonance imaging to assess meningeal lymphatic flow in cognitively normal controls and patients with idiopathic PD (iPD) or atypical Parkinsonian (AP) disorders. We found that patients with iPD exhibited significantly reduced flow through the meningeal lymphatic vessels (mLVs) along the superior sagittal sinus and sigmoid sinus, as well as a notable delay in deep cervical lymph node perfusion, compared to patients with AP. There was no significant difference in the size (cross-sectional area) of mLVs in patients with iPD or AP versus controls. In mice injected with α-synuclein (α-syn) preformed fibrils, we showed that the emergence of α-syn pathology was followed by delayed meningeal lymphatic drainage, loss of tight junctions among meningeal lymphatic endothelial cells and increased inflammation of the meninges. Finally, blocking flow through the mLVs in mice treated with α-syn preformed fibrils increased α-syn pathology and exacerbated motor and memory deficits. These results suggest that meningeal lymphatic drainage dysfunction aggravates α-syn pathology and contributes to the progression of PD. Reduced meningeal lymphatic flow detected in patients with idiopathic Parkinson’s disease compared to patients with atypical Parkinsonian disorders and cognitively normal controls.

The research on decision making driven by product rankings faces challenges due to the rise of extensive positive reviews and the widespread distribution of electronic word of mouth (eWOM) across multiple platforms. There is a limited body of research that examines the impact of platform credibility on the quality of product rankings. Hence, based on the basic uncertain linguistic information (BULI), which enables simultaneous representation of information and its credibility, we investigate the development of a ratings aggregation approach for cross-platform distribution (CPD) with the aim of facilitating decision-making processes, focusing specifically on the aspect of credibility. To begin with, this paper introduces the concept of BULI as a means to effectively represent both product ratings and their corresponding levels of credibility. Subsequently, we proceeded to devise the BULI-based aggregation functions that are well suited for the aggregation of CPD ratings and that can be degraded to the existing operator. In addition, we develop a credibility evaluation index system and credibility calculation model for the platform in order to derive a product BULI matrix consisting of ratings and their corresponding levels of credibility. In this study, we propose two models, namely the feature information-based user weighting model and the BULI distance measure-based technique for order preference by similarity to an ideal solution (BULI-TOPSIS) model, to enhance the product ratings aggregation approach for decision-making purposes. The utilization of the proposed method is exemplified through the case study of passenger car ranking, showcasing its practicality and efficiency.

As a strategic resource for both medicine and essential oil, the healthy development of the Pogostemon cablin industry is crucial for the traditional medicine and fragrance sectors. Bacterial wilt represents one of the most significant threats to patchouli cultivation; however, the molecular mechanisms underlying P. cablin ’s response to bacterial wilt remain unexplored. Here, we conducted transcriptome and metabolome analyses, revealing an increase in the expression of genes associated with lipid pathways and a corresponding rise in the concentration of lipid metabolites in P. cablin following infection by the bacterial wilt pathogen SY1 . Further lipidomics analysis demonstrated a significant upregulation of ceramide levels due to SY1 infection. Additionally, hormone analysis indicated that SY1 significantly induced an increase in abscisic acid (ABA) concentration, accompanied by the upregulation of genes involved in the ABA synthesis pathway and its downstream signaling pathways. Furthermore, we treated P. cablin seedlings with the ceramide synthase inhibitor FB1, which significantly reduced ceramide concentration in P. cablin . FB1 treatment also inhibited the expression of ABA-synthesizing genes, leading to a notable decrease in ABA concentration and downstream pathway genes. These data indicate that ceramides and ABA may participate in P. cablin ’s response to SY1 .

Fractional vegetation cover (FVC) has a significant role in indicating changes in ecosystems and is useful for simulating growth processes and modeling land surfaces. The fine-resolution FVC products represent detailed vegetation cover information within fine grids. However, the long revisit cycle of satellites with fine-resolution sensors and cloud contamination has resulted in poor spatial and temporal continuity. In this study, we propose to derive a spatially and temporally continuous FVC dataset by comparing multiple methods, including the data-fusion method (STARFM), curve-fitting reconstruction (S-G filtering), and deep learning prediction (Bi-LSTM). By combining Landsat and Sentinel-2 data, the integrated FVC was used to construct the initial input of fine-resolution FVC with gaps. The results showed that the FVC of gaps were estimated and time-series FVC was reconstructed. The Bi-LSTM method was the most effective and achieved the highest accuracy (R2 = 0.857), followed by the data-fusion method (R2 = 0.709) and curve-fitting method (R2 = 0.705), and the optimal time step was 3. The inclusion of relevant variables in the Bi-LSTM model, including LAI, albedo, and FAPAR derived from coarse-resolution products, further reduced the RMSE from 5.022 to 2.797. By applying the optimized Bi-LSTM model to Hubei Province, a time series 30 m FVC dataset was generated, characterized by a spatial and temporal continuity. In terms of the major vegetation types in Hubei (e.g., evergreen and deciduous forests, grass, and cropland), the seasonal trends as well as the spatial details were captured by the reconstructed 30 m FVC. It was concluded that the proposed method was applicable to reconstruct the time-series FVC over a large spatial scale, and the produced fine-resolution dataset can support the data needed by many Earth system science studies.

Cervical cancer (CC) remains one of the leading malignancies afflicting females worldwide, with its aetiology associated with long‐term papillomavirus infection. Recent studies have shifted their focus and research attention to the relationship between long non‐coding RNAs (lncRNAs) and CC therapeutic. Thus, the aim of the current study was to investigate the underlying mechanism of lncRNA LINC01305 on the cell invasion, migration and epithelial‐mesenchymal transition (EMT) of CC cells via modulation of the PI3K/Akt signalling pathway by targeting tenascin‐X B (TNXB). The expressions of LINC01305, TNXB, MMP2, MMP9, E‐cadherin, vimentin, PI3K, Akt, p‐PI3K, p‐Akt and TNXB were detected in this study. After which, the cell invasion and migration abilities of the CC cells were determined respectively. Bioinformatics and the application of a dual luciferase reporter gene assay provided verification indicating that TNXB is the target gene of lncRNA LINC01305. Reverse transcription quantitative polymerase chain reaction (RT‐qPCR) and western blot analysis methods revealed that the expressions of MMP2, MMP9, vimentin, PI3K, Akt, p‐PI3K and p‐Akt were decreased following the down‐regulation of LncRNA LINC01305 or overexpression of TNXB. LncRNA LINC01305 silencing or TNXB overexpression was noted to decrease the migration and invasion of SiHa cells. Taken together, the key findings of the current study present evidence suggesting that lncRNA LINC01305 silencing suppresses EMT, invasion and migration via repressing the PI3K/Akt signalling pathway by means of targeting TNXB in CC cells, which ultimately provides novel insight and identification of potential therapeutic targets for CC.

Plasma low-density lipoprotein (LDL) is primarily cleared by LDL receptor (LDLR). LDLR can be proteolytically cleaved to release its soluble ectodomain (sLDLR) into extracellular milieu. However, the proteinase responsible for LDLR cleavage is unknown. Here we report that membrane type 1-matrix metalloproteinase (MT1-MMP) co-immunoprecipitates and co-localizes with LDLR and promotes LDLR cleavage. Plasma sLDLR and cholesterol levels are reduced while hepatic LDLR is increased in mice lacking hepatic MT1-MMP. Opposite effects are observed when MT1-MMP is overexpressed. MT1-MMP overexpression significantly increases atherosclerotic lesions, while MT1-MMP knockdown significantly reduces cholesteryl ester accumulation in the aortas of apolipoprotein E (apoE) knockout mice. Furthermore, sLDLR is associated with apoB and apoE-containing lipoproteins in mouse and human plasma. Plasma levels of sLDLR are significantly increased in subjects with high plasma LDL cholesterol levels. Thus, we demonstrate that MT1-MMP promotes ectodomain shedding of hepatic LDLR, thereby regulating plasma cholesterol levels and the development of atherosclerosis. Elevated plasma LDL cholesterol levels increase the risk of atherosclerotic cardiovascular disease. Here, the authors show that inhibition of MT1-MMP reduces plasma LDL cholesterol levels and the risk of atherosclerosis, indicating the potential of MT1-MMP inhibition as a lipid-lowering therapy.

We propose a novel approach to quantify the contribution of technological diffusion to climate change mitigation. First, we use a parametric model of epidemic diffusion to estimate from micro-level data the determinants and the structure of the networks of diffusion for three key mitigation technologies: electro-mobility, renewable energy and agriculture. We then simulate the propagation of new technological vintages on these networks and quantify the reduction of emissions induced by the diffusion process using a tailored feedback centrality measure labelled “emission centrality”. Finally, we investigate how new forms of international collaboration such as climate clubs can contribute to mitigation by catalysing the adoption of new technologies. Our approach can be used directly to measure the contribution of technological diffusion to mitigation or indirectly by providing estimates of global technological diffusion to integrated assessment models.

Geostationary satellite data enable frequent observations of the Earth’s surface, facilitating the rapid monitoring of land covers and changes. However, optical signals over vegetation, represented by the vegetation index (VI), exhibit an anisotropic effect due to the diurnal variation in the solar angle during data acquisition by geostationary satellites. This effect, typically characterized by the bi-directional reflectance distribution function (BRDF), can introduce uncertainties in vegetation monitoring and the estimation of phenological transition dates (PTDs). To address this, we investigated the diurnal variation in the normalized difference vegetation index (NDVI) with solar angles obtained from geostationary satellites since the image had fixed observation angles. By establishing a temporal conversion relationship between instantaneous NDVI and daily NDVI at the local solar noon (LSNVI), we successfully converted NDVIs obtained at any time during the day to LSNVI, increasing cloud-free observations of NDVI by 34%. Using different statistics of the time series vegetation index, including LSNVI, daily averaged NDVI (DAVI), and angular corrected NDVI (ACVI), we extracted PTD at five typical sites in China. The results showed a difference of up to 41.5 days in PTD estimation, with the highest accuracy achieved using LSNVI. The use of the proposed conversion approach, utilizing time series LSNVI, reduced the root mean square error (RMSE) of PTD estimation by 9 days compared with the use of actual LSNVI. In conclusion, this study highlights the importance of eliminating BRDF effects in geostationary satellite observations and demonstrates that the proposed angular normalization method can enhance the accuracy of time series NDVI in vegetation monitoring.