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Given the substantial emissions of CO2 into the atmosphere, there is a critical need for effective CO2 adsorbents at scale, ideally derived from abundant and sustainable natural resources. In this work, microcrystalline cellulose derived from cotton is used to fabricate cellulose aerogel as porous support via a NaOH/urea‐based dissolution and regeneration process, followed by surface modification with a series of amino silane coupling agents to produce aminated cellulose aerogel as CO2 adsorbent. The as‐synthesized optimal adsorbent exhibits a high CO2 sorption capacity of up to 1.5 and 1.3 mmol g−1 at 0 °C and 25 °C at 1 bar, respectively. Notably, in‐depth analysis shows that the adsorbent achieves an impressive capacity of CO2 uptake of 0.29 mmol g−1 at 25 °C at an exceptionally low CO2 pressure of 0.4 mbar, i.e., under ambient CO2 pressure. It implies its potential use as adsorbent both for the traditional point‐source capture and the direct air capture as an emerging negative emission technology. This study underscores the environmentally friendly, cost‐effective, and biosourced attributes of aminated cellulose aerogel as a compelling alternative for carbon capture, contributing to global initiatives combating CO2 emissions and stressing the key role of sustainable materials in tackling this global environmental challenge. [ABSTRACT FROM AUTHOR]

Objective To analyze the trends in incidence of acutemyocardialinfarction (AMI)in Wuhan from 2016 to 2023, and to provide scientific basis for prevention and control of AMI in Wuhan. Methods The incidence data during 2016-2023 from the Surveillance System of cardiovascular diseases in Wuhan were collected to compute the incidence, age-standardized rate by China population 2010 (ASR-C) and by Segi's world standard population (ASR-W). The annual percent change (APC) and average annual percent change (AAPC) of incidence of AMI were evaluated using Joinpoint regression model, to analyze the incidence trends of AMI. Results The incidence of AMI in Wuhan increased from 56.67/105 in 2016 to 115.29/105 in 2023 (APC = 10.24%, P < 0.05); and ASR-C of AMI increased from 40.45/105 in 2016 to 79.57/105 in 2023 (APC = 9.75%, P < 0.05).The AMI incidence of males(83.65/105) was significantly higher than that of females(34.99/105) from 2016 to 2023 (P < 0.05).The AMI incidence of urban area(62.59/105) was significantly higher than that of rural area(55.35/105)(P < 0.05).The regression model showed that the incidence and ASR-C of AMI in male and female all showed a significant upward trend (all P < 0.05);the incidence and ASR-C of AMI of urban residents all showed a significant upward trend (all P < 0.05), while the trends of rural residents from 2016 to 2020 were not statistically significant, but they all showed a significant upward trend from 2020 to 2023 (all P < 0.05).The incidence of AMI in age groups < 30,30- < 40,40- < 50, 50- < 60, 60- < 70 years and ≥70 years in Wuhan increased at different rates from 2016 to 2023(P < 0.05).Incidence in age groups 30- < 40,40- < 50 years increased rapidly, with the AAPCs of 16.73% and 13.87%, respectively. Conclusion The incidence of AMI in Wuhan showed an upward trend from 2016 to 2023, young adult males are the crucial population for prevention and control. [ABSTRACT FROM AUTHOR]

Over the last two decades, many exciting variable selection methods have been developed for finding a small group of covariates that are associated with the response from a large pool. Can the discoveries by such data mining approaches be spurious due to high dimensionality and limited sample size? Can our fundamental assumptions on exogeneity of covariates needed for such variable selection be validated with the data? To answer these questions, we need to derive the distributions of the maximum spurious correlations given certain number of predictors, namely, the distribution of the correlation of a response variable Y with the best s linear combinations of p covariates X, even when X and Y are independent. When the covariance matrix of X possesses the restricted eigenvalue property, we derive such distributions for both finite s and diverging s, using Gaussian approximation and empirical process techniques. However, such a distribution depends on the unknown covariance matrix of X. Hence, we use the multiplier bootstrap procedure to approximate the unknown distributions and establish the consistency of such a simple bootstrap approach. The results are further extended to the situation where residuals are from regularized fits. Our approach is then applied to construct the upper confidence limit for the maximum spurious correlation and testing exogeneity of covariates. The former provides a baseline for guarding against false discoveries due to data mining and the latter tests whether our fundamental assumptions for high-dimensional model selection are statistically valid. Our techniques and results are illustrated by both numerical examples and real data analysis.

Abstract Background Immune checkpoint blockade resistance narrows the efficacy of cancer immunotherapies, but the underlying mechanism remains elusive. Delineating the inherent mechanisms of anti-PD1 resistance is important to improve outcome of patients with advanced HCC. Method The level of cricTMEM181 was measured in HCC patients with anti-PD1 therapy by RNA sequencing and then confirmed by qPCR and Sanger sequencing. Immune status in tumor microenvironment of HCC patients or mice models was evaluated by flow cytometry and IHC. Exosomes from HCC cell lines were isolated by ultracentrifugation, and their internalization by macrophage was confirmed by immunofluorescence. The underlying mechanism of HCC-derived exosomal circTMEM181 to macrophage was confirmed by SILAC, RNA FISH and RNA immunoprecipitation. The ATP–ADO pathway amplified by HCC–macrophage interaction was evaluated through ATP, AMP and ADO measurement and macrophage-specific CD39 knockout mice. The role of circTMEM181 in anti-PD1 therapy and its clinical significance were also determined in our retrospective HCC cohorts. Results Here, we found that circTMEM181 was elevated in hepatocellular carcinoma (HCC) patients responding poorly to anti-PD1 therapy and in HCC patients with a poor prognosis after operation. Moreover, we also found that high exosomal circTMEM181 favored the immunosuppressive microenvironment and endowed anti-PD1 resistance in HCC. Mechanistically, exosomal circTMEM181 sponged miR-488-3p and upregulated CD39 expression in macrophages. Using macrophage-specific CD39 knockout mice and pharmacologic approaches, we revealed a novel mode of anti-PD1 resistance in HCC. We discovered that cell-specific CD39 expression in macrophages and CD73 expression in HCC cells synergistically activated the eATP–adenosine pathway and produced more adenosine, thereby impairing CD8+ T cell function and driving anti-PD1 resistance. Conclusion In summary, HCC-derived exosomal circTMEM181 contributes to immunosuppression and anti-PD1 resistance by elevating CD39 expression, and inhibiting the ATP–adenosine pathway by targeting CD39 on macrophages can rescue anti-PD1 therapy resistance in HCC. Graphical Abstract

ObjectiveThis study aims to identify reliable prognostic biomarkers for differentiated thyroid cancer (DTC) based on glycolysis-related genes (GRGs), and to construct a glycolysis-related gene model for predicting the prognosis of DTC patients.MethodsWe retrospectively analyzed the transcriptomic profiles and clinical parameters of 838 thyroid cancer patients from 6 public datasets. Single factor Cox proportional risk regression analysis and Least Absolute Shrinkage and Selection Operator (LASSO) were applied to screen genes related to prognosis based on 2528 GRGs. Then, an optimal prognostic model was developed as well as evaluated by Kaplan-Meier and ROC curves. In addition, the underlying molecular mechanisms in different risk subgroups were also explored via The Cancer Genome Atlas (TCGA) Pan-Cancer study.ResultsThe glycolysis risk score (GRS) outperformed conventional clinicopathological features for recurrence-free survival prediction. The GRS model identified four candidate genes (ADM, MKI67, CD44 and TYMS), and an accurate predictive model of relapse in DTC patients was established that was highly correlated with prognosis (AUC of 0.767). In vitro assays revealed that high expression of those genes increased DTC cancer cell viability and invasion. Functional enrichment analysis indicated that these signature GRGs are involved in remodelling the tumour microenvironment, which has been demonstrated in pan-cancers. Finally, we generated an integrated decision tree and nomogram based on the GRS model and clinicopathological features to optimize risk stratification (AUC of the composite model was 0.815).ConclusionsThe GRG signature-based predictive model may help clinicians provide a prognosis for DTC patients with a high risk of recurrence after surgery and provide further personalized treatment to decrease the chance of relapse.

Two-sample U-statistics are widely used in a broad range of applications, including those in the fields of biostatistics and econometrics. In this paper, we establish sharp Cramér-type moderate deviation theorems for Studentized two-sample U-statistics in a general framework, including the two-sample t-statistic and Studentized Mann-Whitney test statistic as prototypical examples. In particular, a refined moderate deviation theorem with second-order accuracy is established for the two-sample t-statistic. These results extend the applicability of the existing statistical methodologies from the one-sample t-statistic to more general nonlinear statistics. Applications to two-sample large-scale multiple testing problems with false discovery rate control and the regularized bootstrap method are also discussed.

Cramér type moderate deviation theorems quantify the accuracy of the relative error of the normal approximation and provide theoretical justifications for many commonly used methods in statistics. In this paper, we develop a new randomized concentration inequality and establish a Cramér type moderate deviation theorem for general self-normalized processes which include many well-known Studentized nonlinear statistics. In particular, a sharp moderate deviation theorem under optimal moment conditions is established for Studentized U-statistics.

Stein's method is a powerful tool for proving central limit theorems along with explicit error bounds in probability theory, where uniform and non-uniform Berry-Esseen bounds spark general interest. Nonlinear statistics, typified by Hoeffding's class of U-statistics, L-statistics, random sums and functions of nonlinear statistics, are building blocks in various statistical inference problems. However, because the standardized statistics often involve unknown nuisance parameters, the Studentized analogues are most commonly used in practice. This paper begins with a brief review of some standard techniques in Stein's method, and their applications in deriving Berry-Esseen bounds and Cramér moderate deviations for nonlinear statistics, and then using the concentration inequality approach, establishes Berry-Esseen bounds for Studentized nonlinear statistics in a general framework. As direct applications, sharp Berry-Esseen bounds for Studentized U- and L- statistics are obtained.