Resveratrol protects against sepsis induced acute kidney injury in mice by inducing Klotho mediated apoptosis inhibition

Purpose: To investigate the mechanism of resveratrol protection against sepsis-induced acute kidney injury in mice. Methods: A sepsis-induced acute kidney injury model was established in mice by cecal ligation and puncture (CLP). Sixty healthy male ICR mice were randomly divided into the sham operation (sham) group, sepsis-induced acute kidney injury model (CLP) group, CLP + low-dose (20 mg/kg) resveratrol treatment (CLP + ResL) group, CLP + high-dose (40 mg/kg) resveratrol treatment (CLP + ResH) group and CLP + Klotho (0.01 mg/kg) treatment (CLP + Klotho) group. All mice were administered treatment on the day after surgery and once every 24 h for 3 days. Various serum biochemical parameters and protein expressions were evaluated. Results: After CLP, the levels of serum creatinine (Scr) and blood urea nitrogen (BUN) increased and the pathology was exacerbated. The protein and mRNA expression levels of Klotho and Bcl-2 decreased, while those of Bax and Caspase-3 increased (p < 0.05). After resveratrol and Klotho protein intervention, Scr and BUN levels recovered, and pathological changes were alleviated. The protein and mRNA expression levels of Klotho and Bcl-2 increased, while those of Bax and Caspase-3 decreased. The conditions of the mice in CLP + ResH group and the CLP + Klotho group improved more significantly than those of the mice in the CLP + ResL group (p < 0.05). Conclusion: Resveratrol upregulates the expression of endogenous Klotho to exert its antiapoptotic effects, which can protect the kidneys of mice against sepsis-induced acute kidney injury. Thus, the compound has potentials for development for protection against acute kidney injury

An exploratory study of the upper middle-class consumer attitudes towards counterfeiting in China

Although counterfeiting has been discussed in the literature, research focusing on the newly-emerged upper-middle class from emerging economies remains scarce. The aim of this exploratory study is to uncover the new upper-middle class consumers’ attitudes towards counterfeiting in China. Qualitative research method was adopted to provide richer and deep information on the research questions. Through semi-structured in-depth interviews with members of the Chinese upper-middle class in Beijing, this study reveals that upper-middle class consumers present a distinctive view in counterfeiting in that they believe counterfeiting not only causes grave welfare related consequences and loss of trust in the legal system, but also seriously interferes with the order of the market

D-STEM: a Design led approach to STEM innovation

Advances in the Science, Technology, Engineering and Maths (STEM) disciplines offer opportunities for designers to propose and make products with advanced, enhanced and engineered properties and functionalities. In turn, these advanced characteristics are becoming increasingly necessary as resources become ever more strained through 21st century demands, such as ageing populations, connected communities, depleting raw materials, waste management and energy supply. We need to make things that are smarter, make our lives easier, better and simpler. The products of tomorrow need to do more with less. The issue is how to maximize the potential for exploiting opportunities offered by STEM developments and how best to enable designers to strengthen their position within the innovation ecosystem. As a society, we need designers able to navigate emerging developments from the STEM community to a level that enables understanding and knowledge of the new material properties, the skill set to facilitate absorption into the design ‘toolbox’ and the agility to identify, manage and contextualise innovation opportunities emerging from STEM developments. This paper proposes the blueprint for a new design led approach to STEM innovation that begins to redefine studio culture for the 21st Century

Colorimetric sensing of copper(II) based on catalytic etching of gold nanoparticles

Based on the catalytic etching of gold nanoparticles (AuNPs), a label-free colorimetric probe was developed for the detection of Cu2+ in aqueous solutions. AuNPs were first stabilized by hexadecyltrimethylammonium bromide in NH3-NH4Cl (0.6 M/0.1 M) solutions. Then thiosulfate (S2O32-) ions were introduced and AuNPs were gradually dissolved by dissolved oxygen. With the further addition of Cu2+, Cu(NH3)(4)(2+) oxidized AuNPs to produce Au(S2O3)(2)(3-) and Cu(S2O3)(3)(5-), while the later was oxidized to Cu(NH3)(4)(2+) again by dissolved oxygen. The dissolving rate of AuNPs was thereby remarkably promoted and Cu2+ acted as the catalyst. The process went on due to the sufficient supply of dissolved oxygen and AuNPs were rapidly etched. Meanwhile, a visible color change from red to colorless was observed. Subsequent tests confirmed such a non-aggregation-based method as a sensitive (LOD= 5.0 nM or 032 ppb) and selective (at least 100-fold over other metal ions except for Pb2+ and Mn2+) way for the detection of Cu2+ (linear range, 10-80 nM). Moreover, our results show that the color change induced by 40 nM Cu2+ can be easily observed by naked eyes, which is particularly applicable to fast on-site investigations. (C) 2013 Elsevier B.V. All rights reserved.Based on the catalytic etching of gold nanoparticles (AuNPs), a label-free colorimetric probe was developed for the detection of Cu2+ in aqueous solutions. AuNPs were first stabilized by hexadecyltrimethylammonium bromide in NH3-NH4Cl (0.6 M/0.1 M) solutions. Then thiosulfate (S2O32-) ions were introduced and AuNPs were gradually dissolved by dissolved oxygen. With the further addition of Cu2+, Cu(NH3)(4)(2+) oxidized AuNPs to produce Au(S2O3)(2)(3-) and Cu(S2O3)(3)(5-), while the later was oxidized to Cu(NH3)(4)(2+) again by dissolved oxygen. The dissolving rate of AuNPs was thereby remarkably promoted and Cu2+ acted as the catalyst. The process went on due to the sufficient supply of dissolved oxygen and AuNPs were rapidly etched. Meanwhile, a visible color change from red to colorless was observed. Subsequent tests confirmed such a non-aggregation-based method as a sensitive (LOD= 5.0 nM or 032 ppb) and selective (at least 100-fold over other metal ions except for Pb2+ and Mn2+) way for the detection of Cu2+ (linear range, 10-80 nM). Moreover, our results show that the color change induced by 40 nM Cu2+ can be easily observed by naked eyes, which is particularly applicable to fast on-site investigations. (C) 2013 Elsevier B.V. All rights reserved

Diversifying Question Generation over Knowledge Base via External Natural Questions

Previous methods on knowledge base question generation (KBQG) primarily focus on enhancing the quality of a single generated question. Recognizing the remarkable paraphrasing ability of humans, we contend that diverse texts should convey the same semantics through varied expressions. The above insights make diversifying question generation an intriguing task, where the first challenge is evaluation metrics for diversity. Current metrics inadequately assess the above diversity since they calculate the ratio of unique n-grams in the generated question itself, which leans more towards measuring duplication rather than true diversity. Accordingly, we devise a new diversity evaluation metric, which measures the diversity among top-k generated questions for each instance while ensuring their relevance to the ground truth. Clearly, the second challenge is how to enhance diversifying question generation. To address this challenge, we introduce a dual model framework interwoven by two selection strategies to generate diverse questions leveraging external natural questions. The main idea of our dual framework is to extract more diverse expressions and integrate them into the generation model to enhance diversifying question generation. Extensive experiments on widely used benchmarks for KBQG demonstrate that our proposed approach generates highly diverse questions and improves the performance of question answering tasks.Comment: 12 pages, 2 figure

Predicting genome-wide redundancy using machine learning

<p>Abstract</p> <p>Background</p> <p>Gene duplication can lead to genetic redundancy, which masks the function of mutated genes in genetic analyses. Methods to increase sensitivity in identifying genetic redundancy can improve the efficiency of reverse genetics and lend insights into the evolutionary outcomes of gene duplication. Machine learning techniques are well suited to classifying gene family members into redundant and non-redundant gene pairs in model species where sufficient genetic and genomic data is available, such as <it>Arabidopsis thaliana</it>, the test case used here.</p> <p>Results</p> <p>Machine learning techniques that combine multiple attributes led to a dramatic improvement in predicting genetic redundancy over single trait classifiers alone, such as BLAST E-values or expression correlation. In withholding analysis, one of the methods used here, Support Vector Machines, was two-fold more precise than single attribute classifiers, reaching a level where the majority of redundant calls were correctly labeled. Using this higher confidence in identifying redundancy, machine learning predicts that about half of all genes in <it>Arabidopsis </it>showed the signature of predicted redundancy with at least one but typically less than three other family members. Interestingly, a large proportion of predicted redundant gene pairs were relatively old duplications (e.g., Ks > 1), suggesting that redundancy is stable over long evolutionary periods.</p> <p>Conclusions</p> <p>Machine learning predicts that most genes will have a functionally redundant paralog but will exhibit redundancy with relatively few genes within a family. The predictions and gene pair attributes for <it>Arabidopsis </it>provide a new resource for research in genetics and genome evolution. These techniques can now be applied to other organisms.</p

VQCNIR: Clearer Night Image Restoration with Vector-Quantized Codebook

Night photography often struggles with challenges like low light and blurring, stemming from dark environments and prolonged exposures. Current methods either disregard priors and directly fitting end-to-end networks, leading to inconsistent illumination, or rely on unreliable handcrafted priors to constrain the network, thereby bringing the greater error to the final result. We believe in the strength of data-driven high-quality priors and strive to offer a reliable and consistent prior, circumventing the restrictions of manual priors. In this paper, we propose Clearer Night Image Restoration with Vector-Quantized Codebook (VQCNIR) to achieve remarkable and consistent restoration outcomes on real-world and synthetic benchmarks. To ensure the faithful restoration of details and illumination, we propose the incorporation of two essential modules: the Adaptive Illumination Enhancement Module (AIEM) and the Deformable Bi-directional Cross-Attention (DBCA) module. The AIEM leverages the inter-channel correlation of features to dynamically maintain illumination consistency between degraded features and high-quality codebook features. Meanwhile, the DBCA module effectively integrates texture and structural information through bi-directional cross-attention and deformable convolution, resulting in enhanced fine-grained detail and structural fidelity across parallel decoders. Extensive experiments validate the remarkable benefits of VQCNIR in enhancing image quality under low-light conditions, showcasing its state-of-the-art performance on both synthetic and real-world datasets. The code is available at https://github.com/AlexZou14/VQCNIR.Comment: This paper is accepted by AAAI202

Learning Policy Levers: Toward Automated Policy Analysis Using Judicial Corpora

To build inputs for end-to-end machine learning estimates of the causal impacts of law, we consider the problem of automatically classifying cases by their policy impact. We propose and implement a semi-supervised multi-class learning model, with the training set being a hand-coded dataset of thousands of cases in over 20 politically salient policy topics. Using opinion text features as a set of predictors, our model can classify labeled cases by topic correctly 91% of the time. We then take the model to the broader set of unlabeled cases and show that it can identify new groups of cases by shared policy impact