Stabilization of Cr(III) wastes by C3S and C3S hydrated matrix : comparison of two incorporation methods

In the present study, the influence of Cr(III) on the properties of C3S and its stabilization in C3S hydrates was investigated by either direct incorporation as Cr2O3 during C3S preparation or introduced as nitrate salt during hydration. Levels of Cr used were from 0.1 to 3.0 wt% of C3S. The effect of Cr on the polymorph and hydration of C3S and its immobilization in the hydrates was detected by means of DTA/TG, XRD, isothermal calorimeter and ICP-AES, etc. When doped during sintering process, Cr caused a C3S polymorph transformation from T1 to T2 and led a decomposition of C3S into C2S and CaO resulting in high f-CaO content. Cr doping showed an obvious promotion effect on the hydration properties. The promotion effect decreased when the Cr addition increased to 3.0 wt%. When Cr was added as nitrate salt, Cr showed a retardation effect on the hydration of C3S due to the formation of Ca2Cr(OH)7 center dot 3H(2)O, which resulted in a high degree of Cr stabilization

Effect of Liuweibuqi capsule, a Chinese patent medicine, on the JAK1/STAT3 pathway and MMP9/TIMP1 in a chronic obstructive pulmonary disease rat model

AbstractObjectiveTo observe effect of Liuweibuqi Capsule, a Traditional Chinese Medicine (TCM), on the janus kinase (JAK)/signal transducer and activator of transcription (STAT) pathway and matrix metalloproteinases (MMPs) in a chronic obstructive pulmonary disease (COPD) rat model with lung deficiency in terms of TCM's pattern differentiation.MethodsRats were randomly divided into a normal group, model group, Liuweibuqi group, Jinshuibao group, and spleen aminopeptidase group (n= 10). Aside from the normal group, all rats were exposed to smoke plus lipopolysaccharide tracheal instillation to establish the COPD model with lung deficiency. Models were established after 28 days and then the normal and model groups were given normal saline (0.09 g/kg), Liuweibuqi group was given Liuweibuqi capsule (0.35 g/kg), Jinshuibao group was given Jinshuibao capsules (0.495 g/kg), and the spleen group was given spleen aminopeptidase (0.33 mg/kg), once a day for 30 days. Changes in symptoms, signs, and lung histology were observed. Lung function was measured with a spirometer. Serum cytokines were detected using enzyme-linked immunosorbent assay, and changes in the JAK/STAT pathway, MMP-9, and MMPs inhibitor 1 (TIMP1) were detected by immunohistochemistry, RT-PCR, and western blotting, respectively.ResultsCompared with the normal group, lung tissue was damaged, and lung function was reduced in the model control group. Additionally, the levels of interleukin (IL)-1β, γ interferon (IFN-γ), and IL-6 were higher, while IL-4 and IL-10 were lower in the model control group than those in the normal group. The expressions of JAK1, STAT3, p-STAT3, and MMP-9 mRNA and protein in lung tissue were higher, and TIMP1 mRNA and protein was lower in the model group compared with the normal group. After treatment, compared with the model group, the expression of inflammatory cytokines was lower in each treatment group, and expressions of JAK/STAT pathway, MMPs were lower. Compared with the positive control groups, the Jinshuibao and spleen aminopeptidase groups, lung function was better, and JAK1, STAT3, and p-STAT3 protein were lower and TIMP1 was higher in the Liuweibuqi group.ConclusionLiuweibuqi capsules can improve the symptoms of COPD possibly by regulating the expression of the JAK1/STAT3 pathway and MMP9/TIMP1

Graph Prompt Learning: A Comprehensive Survey and Beyond

Artificial General Intelligence (AGI) has revolutionized numerous fields, yet its integration with graph data, a cornerstone in our interconnected world, remains nascent. This paper presents a pioneering survey on the emerging domain of graph prompts in AGI, addressing key challenges and opportunities in harnessing graph data for AGI applications. Despite substantial advancements in AGI across natural language processing and computer vision, the application to graph data is relatively underexplored. This survey critically evaluates the current landscape of AGI in handling graph data, highlighting the distinct challenges in cross-modality, cross-domain, and cross-task applications specific to graphs. Our work is the first to propose a unified framework for understanding graph prompt learning, offering clarity on prompt tokens, token structures, and insertion patterns in the graph domain. We delve into the intrinsic properties of graph prompts, exploring their flexibility, expressiveness, and interplay with existing graph models. A comprehensive taxonomy categorizes over 100 works in this field, aligning them with pre-training tasks across node-level, edge-level, and graph-level objectives. Additionally, we present, ProG, a Python library, and an accompanying website, to support and advance research in graph prompting. The survey culminates in a discussion of current challenges and future directions, offering a roadmap for research in graph prompting within AGI. Through this comprehensive analysis, we aim to catalyze further exploration and practical applications of AGI in graph data, underlining its potential to reshape AGI fields and beyond. ProG and the website can be accessed by \url{https://github.com/WxxShirley/Awesome-Graph-Prompt}, and \url{https://github.com/sheldonresearch/ProG}, respectively

Example-based image colorization using locality consistent sparse representation

—Image colorization aims to produce a natural looking color image from a given grayscale image, which remains a challenging problem. In this paper, we propose a novel examplebased image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target grayscale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target grayscale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms state-ofthe-art methods, both visually and quantitatively using a user stud

Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Quantization of transformer language models faces significant challenges due to the existence of detrimental outliers in activations. We observe that these outliers are asymmetric and concentrated in specific channels. To address this issue, we propose the Outlier Suppression+ framework. First, we introduce channel-wise shifting and scaling operations to eliminate asymmetric presentation and scale down problematic channels. We demonstrate that these operations can be seamlessly migrated into subsequent modules while maintaining equivalence. Second, we quantitatively analyze the optimal values for shifting and scaling, taking into account both the asymmetric property and quantization errors of weights in the next layer. Our lightweight framework can incur minimal performance degradation under static and standard post-training quantization settings. Comprehensive results across various tasks and models reveal that our approach achieves near-floating-point performance on both small models, such as BERT, and large language models (LLMs) including OPTs, BLOOM, and BLOOMZ at 8-bit and 6-bit settings. Furthermore, we establish a new state of the art for 4-bit BERT

Clinical Characteristics and Economic Burden of Asthma in China: A Multicenter Retrospective Study

Asthma is a common chronic airway inflammation that produces a healthcare burden on the economy. We aim to obtain a better understanding of the clinical status and disease burden of patients with asthma in China. A retrospective study was carried out based on the computerized medical records in the Jinan Health Medical Big Data Platform between 2011 and 2019 (available data from 38 hospitals). The asthma severity of each patient was assessed retrospectively and categorized as mild, moderate, or severe according to Global Initiative for Asthma 2020 (GINA 2020). The results revealed that the majority (75.0%) of patients suffered from mild asthma. Patients treated with inhaled corticosteroids (ICS)/long-acting beta-agonists (LABA) at emergency department visits had lower frequencies of exacerbations compared with non-ICS/LABA-treated patients. The incidence rates for 1, 2, 3, and 4 exacerbation of the patients treated with ICS/LABA are lower than those treated without ICS/LABA (14.49 vs. 15.01%, 11.94% vs. 19.12%, 6.51% vs.12.92% and 4.10% vs. 9.35%). The difference got a statistical significance Chronic obstructive pulmonary disease (COPD) and gastroesophageal reflux disease (GERD), two comorbidities related to asthma, were risk factors for asthma exacerbation. Finally, patients who suffered from exacerbations produced a heavier economic burden compared to the patients who never suffered exacerbations (mean costs are ￥3,339.67 vs. ￥968.45 separately).  These results provide a reference for clinicians and patients to obtain a better treatment and therapy strategy management for people living with asthma