Objective To assess the immunogenicity and safety of influenza vaccine in patients with systemic lupus erythematosus (SLE). Methods Relevant articles were retrieved from electronic databases. ...Seroprotection rate, seroconversion rate and factors that increase antibody geometric mean titer (GMT) were used as indices to measure the immunogenicity. The safety of vaccine was assessed through monitoring adverse events, which included side effects and SLE exacerbations. We performed a meta-analysis of influenza vaccine seroprotection, seroconversion and adverse effects. SLE exacerbation after vaccination was comprehensively described. We used the Committee for Proprietary Medicinal Products (CPMP) guidelines to determine whether influenza can induce adequate immunogenicity in patients with SLE. Results Eighteen studies with 1966 subjects met the inclusion criteria. At least 565 of the subjects were patients with low-to-moderate SLE Disease Activity Index (SLEDAI) score or stable SLE disease. Compared with the general population, seroprotection rate in SLE patients was significantly decreased in patients with H1N1 odds ratio (OR) = 0.36, 95% confidence interval (CI): 0.27-0.50 and H3N2 vaccination (OR = 0.48, 95% CI: 0.24-0.93), but not influenza B vaccination (OR = 0.55, 95% CI: 0.24-1.25). Seroconversion rate also significantly decreased in patients with H1N1 (OR = 0.39, 95% CI: 0.27-0.57) and influenza B (OR = 0.47, 95% CI: 0.29-0.76) vaccination, but not H3N2 vaccination (OR = 0.62, 95% CI: 0.21-1.79). However, the immunogenicity of influenza vaccine in SLE patients almost reached that of the CPMP guidelines. The OR for side effects (patients versus healthy controls) was 3.24 (95% CI: 0.62-16.76). Among 1966 patients with SLE, 32 experienced mild exacerbation of SLE and five had serious side effects for other reasons. Conclusion Influenza vaccine has moderate effect on protecting patients with SLE. The side effects of influenza vaccine are not serious and are manageable. With consideration of a higher risk of SLE exacerbation and a more severe course of infection among SLE patients, influenza vaccination should be promoted among SLE patients with a low-to-moderate SLEDAI score or stable disease.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background and Purpose Liver fibrosis is characterized by accumulation of extracellular matrix. Our previous study found that osteopontin (OPN) increased in plasma of cirrhotic patients and ...indicative of cirrhosis staging. The present study was designed to investigate the expression of OPN in liver tissues and plasma of cirrhotic patients and further explore the role of OPN in human hepatic stellate cell (HSC) activation. Methods We used immunohistochemical staining and enzyme-linked immunosorbent assay to evaluate the expression level of OPN in liver tissues and plasma from cirrhotic patients, respectively. We produced lentivirus particles and infected target cell to manipulate OPN expression. Infection efficiency was determined by real-time RT-PCR and western blot. Cell proliferation was determined using CCK8 assay, and phenotypes of HSC activation were determined by real-time RT-PCR. OPN promoter activity was determined by dual luciferase reporter assay. Results We found that OPN expression in human cirrhotic liver tissues was upregulated compared to normal controls. In addition, its expression correlated with Child-Pugh classification, MELD score and the occurrence of complications. We further explored OPN level in patients' plasma and showed that its level correlated with transforming growth factor-beta1 (TGF-beta1). In human HSC cell line LX-2, we found that change of OPN expression level could not only affect the proliferation of cells but also the TGF-beta1 mediated HSC activation. Moreover, OPN was increased by TGF-beta1 stimulation and regulated by TGF-beta1 at transcription level. Conclusions OPN is upregulated in liver tissues and plasma of cirrhotic patients and promotes TGF-beta1 mediated HSC activation.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
A small cysteine-rich protein with antimicrobial activity was isolated from pokeweed (Phytolacca americana) seeds and purified to homogeneity. The protein inhibits the growth of several filamentous ...fungi and gram-positive bacteria. The protein was highly basic, with a pl higher than 10. The entire amino acid sequence of the protein was determined to be homologous to antimicrobial protein (AMP) from Mirabilis jalapa. The cDNA encoding the P. americana AMP (Pa-AMP-1) and chromosomal DNA containing the gene were cloned and sequenced. The deduced amino acid sequence shows the presence of a signal peptide at the amino terminus, suggesting that the protein is synthesized as a preprotein and secreted outside the cells. The chromosomal gene shows the presence of an intron located within the region encoding the signal peptide. Southern hybridization showed that there was small gene family encoding Pa-AMP. Immunoblotting showed that Pa-AMP-1 was only present in seeds, and was absent in roots, leaves, and stems. The Pa-AMP-1 protein was secreted into the environment of the seeds during germination, and may create an inhibitory zone against soil-borne microorganisms. The disulfide bridges of Pa-AMP-1 were identified. The three-dimensional modeling of Pa-AMP-1 indicates that the protein has a small cystine-knot folding, a positive patch, and a hydrophobic patch.
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While it is known that cyclic electron flow around photosystem I is an important pathway of photosynthetic electron transfer for converting light energy to chemical energy, some components of cyclic ...electron flow remain to be revealed. Here, we show that fesM, encoding a novel membrane iron-sulfur protein, is essential to cyclic electron flow in the cyanobacterium Synechococcus sp. PCC 7002. The FesM protein is predicted to have a cAMP-binding domain, an NtrC-like domain, a redox sensor motif, and an iron-sulfur (4Fe-4S) motif. Deletion of fesM (fesM-D) led to an inability for Synechococcus cells to grow in the presences of glycerol and 3-(3,4-dichlorophenyl)-1,1-dimethylurea. Photoheterotrophic growth was restored by a complete fesM gene present on a replicable plasmid. A mutant fesM gene encoding a truncated FesM protein lacking the cAMP domain failed to restore the phenotype, suggesting this domain is important to the function of FesM. Measurements of reduction of P700⁺ and the redox state of interphotosystem electron carriers showed that cells had a slower rate of respiratory electron donation to the interphotosystem electron transport chain, and cyclic electron flow around photosystem I in fesM-D was impaired, suggesting that FesM is a critical protein for respiratory and cyclic electron flow. Phosphatase fusion analysis showed that FesM contains nine membrane-spanning helices, and all functional domains of FesM are located on the cytoplasmic face of the thylakoid membranes.
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Machine learning techniques are now integral to the advancement of intelligent urban services, playing a crucial role in elevating the efficiency, sustainability, and livability of urban ...environments. The recent emergence of foundation models such as ChatGPT marks a revolutionary shift in the fields of machine learning and artificial intelligence. Their unparalleled capabilities in contextual understanding, problem solving, and adaptability across a wide range of tasks suggest that integrating these models into urban domains could have a transformative impact on the development of smart cities. Despite growing interest in Urban Foundation Models~(UFMs), this burgeoning field faces challenges such as a lack of clear definitions, systematic reviews, and universalizable solutions. To this end, this paper first introduces the concept of UFM and discusses the unique challenges involved in building them. We then propose a data-centric taxonomy that categorizes current UFM-related works, based on urban data modalities and types. Furthermore, to foster advancement in this field, we present a promising framework aimed at the prospective realization of UFMs, designed to overcome the identified challenges. Additionally, we explore the application landscape of UFMs, detailing their potential impact in various urban contexts. Relevant papers and open-source resources have been collated and are continuously updated at https://github.com/usail-hkust/Awesome-Urban-Foundation-Models.
The dynamic behavior of a reciprocating compressor transmission mechanism with joint clearance is studied in this paper. A transmission mechanism virtual prototype model is built under software ...ADAMS. Revolute joint between connecting rod and crosshead pin is modeled with clearance by contact model based on the so called Impact-function, and four processes of cylinder pressure load is simulated by the IF function. Cylinder pressure load, amplitude of joint clearance, crank speed and flexibility of connecting rod is taken as influence factors. Four different cases were simulated. Joint contact forces, accelerations of compressor block and journal centre path are used to analyze the dynamic behavior. The presented results show that these factors play a significant role of dynamic behavior of mechanisms.
Annotating user interfaces (UIs) that involves localization and classification of meaningful UI elements on a screen is a critical step for many mobile applications such as screen readers and voice ...control of devices. Annotating object icons, such as menu, search, and arrow backward, is especially challenging due to the lack of explicit labels on screens, their similarity to pictures, and their diverse shapes. Existing studies either use view hierarchy or pixel based methods to tackle the task. Pixel based approaches are more popular as view hierarchy features on mobile platforms are often incomplete or inaccurate, however it leaves out instructional information in the view hierarchy such as resource-ids or content descriptions. We propose a novel deep learning based multi-modal approach that combines the benefits of both pixel and view hierarchy features as well as leverages the state-of-the-art object detection techniques. In order to demonstrate the utility provided, we create a high quality UI dataset by manually annotating the most commonly used 29 icons in Rico, a large scale mobile design dataset consisting of 72k UI screenshots. The experimental results indicate the effectiveness of our multi-modal approach. Our model not only outperforms a widely used object classification baseline but also pixel based object detection models. Our study sheds light on how to combine view hierarchy with pixel features for annotating UI elements.
With the increasing penetration of machine learning applications in critical decision-making areas, calls for algorithmic fairness are more prominent. Although there have been various modalities to ...improve algorithmic fairness through learning with fairness constraints, their performance does not generalize well in the test set. A performance-promising fair algorithm with better generalizability is needed. This paper proposes a novel adaptive reweighing method to eliminate the impact of the distribution shifts between training and test data on model generalizability. Most previous reweighing methods propose to assign a unified weight for each (sub)group. Rather, our method granularly models the distance from the sample predictions to the decision boundary. Our adaptive reweighing method prioritizes samples closer to the decision boundary and assigns a higher weight to improve the generalizability of fair classifiers. Extensive experiments are performed to validate the generalizability of our adaptive priority reweighing method for accuracy and fairness measures (i.e., equal opportunity, equalized odds, and demographic parity) in tabular benchmarks. We also highlight the performance of our method in improving the fairness of language and vision models. The code is available at https://github.com/che2198/APW.
Large Language Models (LLMs) have become integral to a wide spectrum of applications, ranging from traditional computing tasks to advanced artificial intelligence (AI) applications. This widespread ...adoption has spurred extensive research into LLMs across various disciplines, including the social sciences. Notably, studies have revealed that LLMs possess emotional intelligence, which can be further developed through positive emotional stimuli. This discovery raises an intriguing question: can negative emotions similarly influence LLMs, potentially enhancing their performance? In response to this question, we introduce NegativePrompt, a novel approach underpinned by psychological principles, involving ten specifically designed negative emotional stimuli. We embark on rigorous experimental evaluations of five LLMs including Flan-T5-Large, Vicuna, Llama 2, ChatGPT, and GPT-4, across a set of 45 tasks. The results are revealing: NegativePrompt markedly enhances the performance of LLMs, evidenced by relative improvements of 12.89% in Instruction Induction tasks and 46.25% in BIG-Bench tasks. Moreover, we conduct attention visualization experiments to decipher the underlying mechanisms of NegativePrompt's influence. Our research contributes significantly to the understanding of LLMs and emotion interaction, demonstrating the practical efficacy of NegativePrompt as an emotion-driven method and offering novel insights for the enhancement of LLMs in real-world applications. The code is available at https://github.com/wangxu0820/NegativePrompt.
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