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Effect and safety of sivelestat on acute severe pancreatitis with systemic inflammatory response syndrome: a retrospective study
Digital PCR detection of microsporidia in household pipe water of patients with microsporidial keratitis
Association between gut microbiota and short-chain fatty acids in children with obesity
The progression of basaltic–rhyolitic melt storage at Yellowstone Caldera
Engineering ultra-strong electron-phonon coupling and nonclassical electron transport in crystalline gold with nanoscale interfaces
AbstractElectrical resistivity in good metals, particularly noble metals such as gold (Au), silver (Ag), or copper, increases linearly with temperature (T) for T > ΘD, where ΘD is the Debye temperature. This is because the coupling (λ) between the electrons and the lattice vibrations, or phonons, in these metals is weak, with λ ~ 0.1−0.2. In this work, we outline a nanostructuring strategy of crystalline Au where this concept of metallic transport breaks down. We show that by embedding a distributed network of ultra-small Ag nanoparticles (AgNPs) of radius ~ 1–2 nm inside a crystalline Au shell, the electron-phonon interaction can be enhanced, with an effective λ as high as ≈ 20. With increasing AgNP density, the electrical resistivity deviates from T-linearity and approaches a saturation to the Mott-Ioffe-Regel scale ρMIR ~ ha/e2 for both disorder (T → 0) and phonon (T ≫ ΘD)-dependent components of resistivity (here, a = 0.3 nm, is the lattice constant of Au).
Modulation of glymphatic system by visual circuit activation alleviates memory impairment and apathy in a mouse model of Alzheimer’s disease
Predicting learning achievement using ensemble learning with result explanation
Predicting learning achievement is a crucial strategy to address high dropout rates. However, existing prediction models often exhibit biases, limiting their accuracy. Moreover, the lack of interpretability in current machine learning methods restricts their practical application in education. To overcome these challenges, this research combines the strengths of various machine learning algorithms to design a robust model that performs well across multiple metrics, and uses interpretability analysis to elucidate the prediction results. This study introduces a predictive framework for learning achievement based on ensemble learning techniques. Specifically, six distinct machine learning models are utilized to establish a base learner, with logistic regression serving as the meta learner to construct an ensemble model for predicting learning achievement. The SHapley Additive exPlanation (SHAP) model is then employed to explain the prediction results. Through the experiments on XuetangX dataset, the effectiveness of the proposed model is verified. The proposed model outperforms traditional machine learning and deep learning model in terms of prediction accuracy. The results demonstrate that the ensemble learning-based predictive framework significantly outperforms traditional machine learning methods. Through feature importance analysis, the SHAP method enhances model interpretability and improves the reliability of the prediction results, enabling more personalized interventions to support students.
Complement classical and alternative pathway activation contributes to diabetic kidney disease progression: a glomerular proteomics on kidney biopsies
New formyl indole derivatives based on thiobarbituric acid and their nano-formulations; synthesis, characterization, parasitology and histopathology investigations
Assessment of changes in soil contact stress depending on tractor tire parameters
High-resolution genomic history of early medieval Europe
AbstractMany known and unknown historical events have remained below detection thresholds of genetic studies because subtle ancestry changes are challenging to reconstruct. Methods based on shared haplotypes1,2 and rare variants3,4 improve power but are not explicitly temporal and have not been possible to adopt in unbiased ancestry models. Here we develop Twigstats, an approach of time-stratified ancestry analysis that can improve statistical power by an order of magnitude by focusing on coalescences in recent times, while remaining unbiased by population-specific drift. We apply this framework to 1,556 available ancient whole genomes from Europe in the historical period. We are able to model individual-level ancestry using preceding genomes to provide high resolution. During the first half of the first millennium ce, we observe at least two different streams of Scandinavian-related ancestry expanding across western, central and eastern Europe. By contrast, during the second half of the first millennium ce, ancestry patterns suggest the regional disappearance or substantial admixture of these ancestries. In Scandinavia, we document a major ancestry influx by approximately 800 ce, when a large proportion of Viking Age individuals carried ancestry from groups related to central Europe not seen in individuals from the early Iron Age. Our findings suggest that time-stratified ancestry analysis can provide a higher-resolution lens for genetic history.
2.5-dimensional covalent organic frameworks
AbstractCovalently bonded crystalline substances with micropores have broad applications. Covalent organic frameworks (COFs) are representative of such substances. They have so far been classified into two-dimensional (2D) and three-dimensional (3D) COFs. 2D-COFs have planar shapes useful for broad purposes, but obtaining good crystals of 2D-COFs with sizes larger than 10 μm is significantly challenging, whereas yielding 3D-COFs with high crystallinity and larger sizes is easier. Here, we show COFs with 2.5-dimensional (2.5D) skeletons, which are microscopically constructed with 3D bonds but have macroscopically 2D planar shapes. The 2.5D-COFs shown herein achieve large single-crystal sizes above 0.1 mm and ultrahigh-density primary amines regularly allocated on and pointing perpendicular to the covalently-bonded network plane. Owing to the latter nature, the COFs are promising as CO2 adsorbents that can simultaneously achieve high CO2/N2 selectivity and low heat of adsorption, which are usually in a mutually exclusive relationship. 2.5D-COFs are expected to broaden the frontier and application of covalently bonded microporous crystalline systems.
Polyolefin reweaved ultra-micropore membrane for CO2 capture
Prognostic significance of total choline on in-vivo proton MR spectroscopy for prediction of late recurrence in patients with hormone receptor-positive, HER2-negative early breast cancer
Purpose In-vivo proton magnetic resonance spectroscopy (MRS) is a non-invasive method of analyzing choline metabolism that has been used to predict breast cancer prognosis. A strong choline peak may be a surrogate for aggressive tumor biology but its clinical relevance is unclear. The present study assessed whether total choline (tCho), as measured by proton MRS, can predict late recurrence in patients with hormone receptor (HR)-positive, HER2-negative early breast cancer. Methods The study cohort included 261 HR+/HER2- breast cancer patients who underwent diagnostic single-voxel proton MRS (3.0T scanner) prior to first-line surgery from March 2011 to July 2014. The relationships between tCho compound peak integral (tChoi) values and others prognostic factor were analyzed, as were the effects of tChoi on 10-year disease-free survival (DFS) and overall survival (OS). The clinical significance of tChoi was also analyzed using Harrell’s C-index. Results Mean tChoi in HR+/HER2- study group was 15.47 and we set the cut-off for tChoi at 15 for survival analysis. 10-year DFS differed significantly between tChoi <15 and ≥15 (p = 0.017), with differences differing significantly for late (5–10 years; p = 0.02) but not early (0–5 years; p = 0.323) recurrence. Cox regression analysis showed that tChoi was significantly predictive of 10-year DFS (p = 0.046, OR 2.69) and tended to be predictive of late recurrence (HR 4.36, p = 0.066). Harrell’s C-index showed that the Ki-67 index (AUC = 0.597) and lymphovascular invasion (AUC = 0.545) were also predictive of survival, with the addition of normalized tChoi improving the AUC to 0.622 (p = 0.014), indicating better predictive power. Conclusion tChoi determined by in vivo MRS was predictive of prognosis in patients with HR+/HER2- early breast cancer. This parameter may serve as a valuable, non-invasive tool to predict prognosis when combined with other known prognostic factors.
Influence of prolong curing, freeze thaw cycles on strength and compaction condition on water retention behaviour of bamboo biochar amended soils
Stakeholder diversity matters: employing the wisdom of crowds for data-poor fisheries assessments
AbstractEmbracing local knowledge is vital to conserve and manage biodiversity, yet frameworks to do so are lacking. We need to understand which, and how many knowledge holders are needed to ensure that management recommendations arising from local knowledge are not skewed towards the most vocal individuals. Here, we apply a Wisdom of Crowds framework to a data-poor recreational catch-and-release fishery, where individuals interact with natural resources in different ways. We aimed to test whether estimates of fishing quality from diverse groups (multiple ages and years of experience), were better than estimates provided by homogenous groups and whether thresholds exist for the number of individuals needed to capture estimates. We found that diversity matters; by using random subsampling combined with saturation principles, we determine that targeting 31% of the survey sample size captured 75% of unique responses. Estimates from small diverse subsets of this size outperformed most estimates from homogenous groups; sufficiently diverse small crowds are just as effective as large crowds in estimating ecological state. We advocate for more diverse knowledge holders in local knowledge research and application.
Regional patterns and climatic predictors of viruses in honey bee (Apis mellifera) colonies over time
Reply to: Limitations of ice cores in reconstructing temperature seasonality
Experimental identification of topological defects in 2D colloidal glass
AbstractTopological defects are singularities within a field that cannot be removed by continuous transformations. The definition of these irregularities requires an ordered reference configuration, calling into question whether they exist in disordered materials, such as glasses. However, recent work suggests that well-defined topological defects emerge in the dynamics of glasses, even if they are not evident in the static configuration. In this study, we reveal the presence of topological defects in the vibrational eigenspace of a two-dimensional experimental colloidal glass. These defects strongly correlate with the vibrational features and spatially correlate with each other and structural “soft spots”, more prone to plastic flow. This work experimentally confirms the existence of topological defects in disordered systems revealing the complex interplay between topology, disorder, and dynamics.