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Taking top global energy companies as the epitome, this paper investigates the risk formulation mechanism of the international energy market under the impact of large shocks. We first use the machine learning method in (Liu and Pun, 2022) to calculate the systematic risk level - EMES - for each energy company. Then use network analysis methods to explore the internal risks due to risk comovement among top energy companies. Finally, a dynamic quantile regression model(DNQR) is used to investigate the external risks occasioned by network effects, individual company characteristics, and market environment. Our research finds that the method we use can capture the risk profile of the energy market under different major shocks. Secondly, we find that the risk contagion in the energy market exhibits geographical clustering characteristics, and certain firm-specific factors and market environmental factors of the company have a significant impact on the tail risk of the company. Our research can provide reference and guidance for risk management in the energy market.

This paper developed a super-parametric shell element to establish the rotating cylindrical shell model by employing the finite element method. Considering parameter uncertainties, the dynamic response of the rotating cylindrical shell is carried out. The accuracy of the model is validated by experiment and commercial software ANSYS. The multivariate Chebyshev polynomial interval analysis method is proposed to solve the multi-parameter uncertainty problem of the rotating cylindrical shell. It has enough precision and significant advantages in computational efficiency through comparison with the scanning method. Young's modulus, rotating speed, modal damping coefficient, excitation amplitude, geometric size and coupling stiffness are considered as uncertain parameters. Attention is given to the effects of different uncertain parameters on the dynamic response of the rotating cylindrical shell.

Triple-negative breast cancer (TNBC) is characterized by a more aggressive clinical course with extensive inter- and intra-tumour heterogeneity. Combination of single-cell and bulk tissue transcriptome profiling allows the characterization of tumour heterogeneity and identifies the association of the immune landscape with clinical outcomes. We identified inter- and intra-tumour heterogeneity at a single-cell resolution. Tumour cells shared a high correlation amongst stemness, angiogenesis, and EMT in TNBC. A subset of cells with concurrent high EMT, stemness and angiogenesis was identified at the single-cell level. Amongst tumour-infiltrating immune cells, M2-like tumour-associated macrophages (TAMs) made up the majority of macrophages and displayed immunosuppressive characteristics. CIBERSORT was applied to estimate the abundance of M2-like TAM in bulk tissue transcriptome file from The Cancer Genome Atlas (TCGA). M2-like TAMs were associated with unfavourable prognosis in TNBC patients. A TAM-related gene signature serves as a promising marker for predicting prognosis and response to immunotherapy. Two commonly used machine learning methods, random forest and SVM, were applied to find the genes that were mostly associated with M2-like TAM densities in the gene signature. A neural network-based deep learning framework based on the TAM-related gene signature exhibits high accuracy in predicting the immunotherapy response.

Mast cells are a major component of the immune microenvironment in tumour tissues and modulate tumour progression by releasing pro‐tumorigenic and antitumorigenic molecules. Regarding the impact of mast cells on the outcomes of patients with lung adenocarcinoma (LUAD) patient, several published studies have shown contradictory results. Here, we aimed at elucidating the role of mast cells in early‐stage LUAD. We found that high mast cell abundance was correlated with prolonged survival in early‐stage LUAD patients. The mast cell‐related gene signature and gene mutation data sets were used to stratify early‐stage LUAD patients into two molecular subtypes (subtype 1 and subtype 2). The neural network‐based framework constructed with the mast cell‐related signature showed high accuracy in predicting response to immunotherapy. Importantly, the prognostic mast cell‐related signature predicted the survival probability and the potential relationship between TP53 mutation, c‐MYC activation and mast cell activities. The meta‐analysis confirmed the prognostic value of the mast cell‐related gene signature. In summary, this study might improve our understanding of the role of mast cells in early‐stage LUAD and aid in the development of immunotherapy and personalized treatments for early‐stage LUAD patients. Mast cell abundance and a mast cell‐related signature were correlated with survival in early‐stage lung adenocarcinoma patients. The mast cell‐related signature‐based neural network showed high accuracy in predicting response to immunotherapy.

Parallel acquisition-readout structured-illumination microscopy (PAR-SIM) was designed for high-speed raw data acquisition. By utilizing an xy-scan galvo mirror set, the raw data is projected onto different areas of the camera, enabling a fundamentally stupendous information spatial-temporal flux.

Nanoaperture optical tweezers (NOTs) were used to analyze conalbumin in various forms. By analyzing the power spectrum of the NOT-transmitted laser signal, differences between iron and iron-free conalbumin were observed; the corner frequency extrapolated to zero-laser power was significantly larger in magnitude for conalbumin with iron, which was interpreted as coming from the enhanced electrostatic interactions close to the surface of the nanoaperture. Conalbumin in a diluted, but otherwise unprocessed, egg white sample showed the same behavior as purified iron-free conalbumin. Dynamic two-state transitions in the NOT signal were observed for iron-free conalbumin and conalbumin in egg white samples. We used this to determine the dominant state as a function of temperature, with one state showing a maximum occupancy around 30.4 °C. Deconvolution of the probability distribution function was used to find the energy landscape associated with this two-state transition. This work shows the potential of NOTs to see variations with metal ion binding, including conformational dynamics related to the binding at timescales not accessible to other methods.

Alzheimer’s disease (AD) is a complex degenerative disease of the central nervous system, and elucidating its pathogenesis remains challenging. In this study, we used the inverse-variance weighted (IVW) model as the major analysis method to perform hypothesis-free Mendelian randomization (MR) analysis on the data from MRC IEU OpenGWAS (18,097 exposure traits and 16 AD outcome traits), and conducted sensitivity analysis with six models, to assess the robustness of the IVW results, to identify various classes of risk or protective factors for AD, early-onset AD, and late-onset AD. We generated 400,274 data entries in total, among which the major analysis method of the IVW model consists of 73,129 records with 4840 exposure traits, which fall into 10 categories: Disease, Medical laboratory science, Imaging, Anthropometric, Treatment, Molecular trait, Gut microbiota, Past history, Family history, and Lifestyle trait. More importantly, a freely accessed online platform called MRAD ( https://gwasmrad.com/mrad/ ) has been developed using the Shiny package with MR analysis results. Additionally, novel potential AD therapeutic targets (CD33, TBCA, VPS29, GNAI3, PSME1) are identified, among which CD33 was positively associated with the main outcome traits of AD, as well as with both EOAD and LOAD. TBCA and VPS29 were negatively associated with the main outcome traits of AD, as well as with both EOAD and LOAD. GNAI3 and PSME1 were negatively associated with the main outcome traits of AD, as well as with LOAD, but had no significant causal association with EOAD. The findings of our research advance our understanding of the etiology of AD.

The three-dimensional structures of hyperbranched materials have made them attractive in many important applications. However, the preparation of hyperbranched materials remains challenging. The hyperbranched materials from addition polymerization have gained attention, but are still confined to only a low level of branching and often low yield. Moreover, the complication of synthesis only allows a few specialized monomers and inimers to be used. Here we report a ‘Vinyl Oligomer Combination’ strategy; a versatile approach that overcomes these difficulties and allows facile synthesis of highly branched polymeric materials from readily available multi-vinyl monomers, which have long been considered as formidable starting materials in addition polymerization. We report the alteration of the growth manner of polymerization by controlling the kinetic chain length, together with the manipulation of chain growth conditions, to achieve veritable hyperbranched materials, which possess nearly 70% branch ratios as well as numerous vinyl functional groups. Hyperbranched polymers have desirable properties as novel materials, and may be synthesized from homopolymerization of multi-vinyl momomers, although this can form insoluble products at low conversions. Here, the authors produce hyperbranched polymers in high yield owing to a kinetic control mechanism.

Patients with early-stage lung adenocarcinoma (LUAD) exhibit different overall survival (OS) rates and immunotherapy responses. Understanding the immune landscape facilitates the personalized treatment of LUAD. The immune cell populations in tumour tissues were quantified to depict the immune landscape in early-stage LUAD patients in The Cancer Genome Atlas (TCGA). Early-stage LUAD patients in three immune clusters identified by the immune landscape exhibited different survival potentials. A prognostic immune-related gene signature was built to predict the survival of early-stage LUAD patients. Several machine learning methods (support vector machine, naive Bayes, random forest, and neural network-based deep learning) were applied to train the classifiers to identify the immune clusters in early-stage LUAD based on the gene signature. The four classifiers exhibited a robust effect in identifying the immune clusters. A random forest regression model identified that TP53 was the most important gene mutation associated with the immune-related signature. Furthermore, a decision tree and a nomogram were constructed based on the immune-related gene signature and clinicopathological traits to improve risk stratification and quantify risk assessment for individual patients. Five external test cohorts were applied to validate the accuracy of the immune-related signature. Our study might contribute to the development of immunotherapy and the personalized treatment of early-stage LUAD.Key messagesImmune landscape correlates with the clinical outcome of early-stage adenocarcinoma (LUAD).Machine learning methods identifies a prognostic gene signature to predict the survival and prognosis of early-stage LUAD.TP53 gene mutation status correlates with the immune landscape in early-stage LUAD.

The mechanistic target of rapamycin complex 1 (mTORC1) is a crucial regulator of cell growth. It senses nutrient signals and adjusts cellular metabolism accordingly. Deregulation of mTORC1 has been associated with metabolic diseases, cancer, and aging. Amino acid signals are transduced to mTORC1 through sensor proteins and two protein complexes named GATOR1 and GATOR2. In this study, we identify VWCE (von Willebrand factor C and EGF domains) as a negative regulator of amino acid-dependent mTORC1 signaling. Knockdown of VWCE promotes mTORC1 activity even in the absence of amino acids. VWCE interacts with the KICSTOR complex to facilitate the recruitment of GATOR1 to the lysosomes. Bioinformatic analysis reveals that expression of VWCE is reduced in prostate cancer. More importantly, overexpression of VWCE inhibits the development of prostate cancer. Therefore, VWCE may serve as a potential therapeutic target for the treatment of prostate cancers. mTORC1 adapts cellular metabolism in response to nutrient signals. Here, the authors identify VWCE as a negative regulator of amino acid-dependent mTORC1 signaling and a potential as a therapeutic target in prostate cancer treatments.