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High glycosylated serum protein to high density lipoprotein cholesterol ratios are predictive of worse acute on chronic liver failure prognoses
Author Correction: Recapitulation of premature ageing with iPSCs from Hutchinson–Gilford progeria syndrome
Targeted plasma proteomics reveals organ damage signatures of AIDS- and noncommunicable disease-related deaths in people with HIV
Smoking status and voting behaviour and intentions in countries of the former Soviet Union
Abstract Smokers experience multiple disadvantages throughout their lives, yet there is another disadvantage, political, that is less widely recognised. Smokers are less likely to vote but only so far in studies conducted in Western democratic regimes. This cross-sectional study aimed to examine the association between current smoking and voting behaviour and intentions in nine countries of the former Soviet Union (FSU). Data were analysed from 18,000 individuals aged ≥ 18 in Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia and Ukraine, collected in the Health in Times of Transition (HITT) survey in 2010/11. Information was obtained on smoking status and voting behaviour and intentions. In a fully adjusted logistic regression analysis, current smoking was associated with significantly higher odds of ‘never voting’ (not having voted in the past or intending to vote in future) in the pooled sample (OR: 1.29, 95% CI 1.13–1.47). In stratified analyses, smoking was associated with never voting in women but not men and in young but not middle-aged or older adults. The smoking-never voting association was observed in flawed democracies (OR: 1.57, 95% CI 1.07–2.32) and hybrid regimes (OR: 1.31, 95% CI 1.08–1.59) but not in authoritarian regimes (OR: 1.02, 95% CI 0.81–1.29). Smoking is associated with never voting in these FSU countries although not in all population subgroups or types of political regime. A necessary task for future research will be determining the factors associated with not voting among smokers in these countries.
Internal lattice oxygen sites invert product selectivity in electrocatalytic alkyne hydrogenation over copper catalysts
Pan-cancer analysis reveals immunological and prognostic significance of CCT5 in human tumors
Abstract The chaperonin containing TCP1 subunit 5 (CCT5) is believed to function as a tumor driver. However, a systematic pan-cancer analysis of CCT5 is still lacking. Therefore, this study aimed to identify the potential role of CCT5 in different types of tumors. This study comprehensively investigated the gene expression, proteomic expression, immune infiltration, DNA methylation, genetic alterations, correlation with TMB and MSI, drug sensitivity, enrichment analysis, and prognostic significance of CCT5 in 33 different tumors based on the TIMER2.0, GEPIA2, UALCAN, SMART, cBioPortal, GSCA databases, and TCGAplot R package. The results revealed significant CCT5 overexpression in most tumors and was significantly associated with poor OS and DFS in different tumor types. Reduced promoter and N-shore methylation of CCT5, indicating its potential oncogenic and epigenetic roles. Amplification was the most common type of CCT5 alterations. Immune infiltration analysis revealed a strong correlation between CCT5 and different immune cells. CCT5 exhibited a significant correlation with TMB and MSI in KIRC and STAD. Furthermore, enrichment analysis revealed associations between CCT5 and cell cycle pathway and various cellular functions. These findings suggested that CCT5 might serve as a potential prognostic biomarker and target for immunotherapy in various cancers.
In-sensor compressing via programmable optoelectronic sensors based on van der Waals heterostructures for intelligent machine vision
A novel temporal classification prototype network for few-shot bearing fault detection
Abstract In the process of industrial production, bearing fault detection has always been a hot issudza20000528@163.comsolved. At present, the problem of less fault data samples in the field of fault detection has caused great trouble to the research of deep learning. In the application of industrial fault detection, which is difficult to obtain massive data, it is easy to lead to the lack of fitting of neural network training and many generalization problems. To solve the above problems, this paper proposes an improved and more efficient method of few-shot supervised learning, which is called the Temporal Classification Prototype Network (TCPN). This model is designed to maintain both training efficacy and generalization capabilities under conditions of data scarcity. Initially, Fourier transform is employed to accentuate the frequency domain characteristics of the fault section in the bearing signal before it is input into the model, thereby enabling the subsequent model to concentrate on distinguishing between normal and fault signals. Subsequently, discrete data sample points are transformed into points within the feature space via our Enhanced Temporal Convolutional Network(ETCN). In our investigation, we utilize the features of the support set as anchors within the feature space and employ similarity measures as the basis for classification, thus developing a more effective comparative learning classifier known as the ContractSim Classifier (CSC). Within the CSC, the model learns the data features of the query set, which are then back-propagated to refine our model. The proposed TCPN model has been evaluated across four standard bearing datasets, corroborating its few-shot learning proficiency through k-shot experiments. In comparative model experiments, our TCPN outperforms baseline models, while the ablation study confirms the rationality and robustness of our module integration.
JointPRS: A data-adaptive framework for multi-population genetic risk prediction incorporating genetic correlation
Monitoring water percolation in a laboratory compacted soil dam using time-lapse electrical resistivity tomography
Correlation measurement of propagating microwave photons at millikelvin
Abstract Microwave photons are essential carriers of quantum information in several promising platforms for quantum computing. However, measurement of the quantum statistical properties of microwave photons is demanding owing to their low energy relative to thermal fluctuations of any room-temperature detector, and phase-insensitive voltage amplification necessarily adds noise. Here, we overcome this trade-off with a nanobolometer that directly measures the photon statistics at millikelvin. Using a cryogenic temperature-controlled blackbody radiator, we demonstrate the detection of the mean photon number $$\langle \hat{n}\rangle$$ ⟨ n ̂ ⟩ and reveal the expected photon number variance $${(\Delta n)}^{2}=\langle \hat{n}\rangle \left(\langle \hat{n}\rangle+1\right)$$ ( Δ n ) 2 = ⟨ n ̂ ⟩ ⟨ n ̂ ⟩ + 1 , following the Bose–Einstein distribution. By engineering the coherent and incoherent proportions of the input field, we observe a transition between super-Poissonian and Poissonian statistics from the bolometric second-order correlation measurements. This technique is poised to serve in fundamental tests of quantum mechanics and function as a scalable readout solution for a quantum information processor.