Chloroquine prevents acute kidney injury induced by lipopolysaccharide in rats via inhibition of inflammatory factors

Purpose: To investigate the role of chloroquine (CQ) in lipopolysaccharide (LPS)-induced renal injury in rats.Methods: Rats were assigned to one of four groups (n = 10). Control group was only given saline solution, whereas the model control, LPS + CQ, and LPS + yohimbine (YOH) + CQ groups were administered LPS intraperitoneally. At the end of the study, blood urea nitrogen (BUN) and creatinine (Cr) levels were determined.Results: CQ treatment significantly decreased the blood concentrations of tissue necrosis factor alpha (TNF-α), interleukin-6 (IL-6), IL-18, BUN, and Cr in the model control rats. There were also significant decreases in the levels of high mobility group protein 1 and kidney injury molecule-1 in the renal injury rats compared to the model control group. However, the inhibitory effects of CQ in the LPS-treated rats were blocked by treatment with YOH, an α-2-adrenergic receptor antagonist.Conclusions: Treatment with CQ attenuates LPS-induced renal injury by inhibiting inflammatory response.Keywords: Creatinine, Chloroquine, Inflammatory reactions, Kidney injury, Lipopolysaccharid

Compression Behaviour of Natural and Reconstituted Clays

International audienceThe intercept of the log(1+e) - logσv' straight line is introduced to describe the effect of the starting point on the compressibility of natural and reconstituted clays. It is found that when the effective stress exceeds the remoulded yield stress, the compression behaviour of reconstituted clays is controlled solely by the water content at the remoulded yield stress and the liquid limit. Comparison of the compression behaviour of natural and reconstituted clays indicates that their difference in compressibility is caused by soil structure and the difference in water content at the compression starting point. The compression behaviour of natural clays can be classified into three regimes: 1) the pre-yield regime characterised by small compressibility with soil structure restraining the deformation up to the consolidation yield stress; 2) the transitional regime characterised by a gradual loss of soil structure when the effective stress is between the consolidation yield stress and the transitional stress; and 3) the post-transitional regime characterised by the same change law in compression behaviour as reconstituted clays when the effective stress is higher than the transitional stress. For the investigated clays, the transitional stress is 1.0-3.5 times the consolidation yield stress. The compression index varies solely with the void ratio at an effective stress of 1.0 kPa for both natural clays in post-transitional regime and reconstituted clays when the effective stress exceeds the remoulded yield stress, and when compressed in such cases the compression curves of both natural clays and reconstituted clays can be normalised well to a unique line using the void index

The epitope of the VP1 protein of porcine parvovirus

Porcine parvovirus (PPV) is the major causative agent in a syndrome of reproductive failure in swine. Much has been learned about the structure and function of PPV in recent years, but nothing is known about the epitopes of the structural protein VP1, which is an important antigen of PPV. In this study, the monoclonal antibody C4 against VP1 of PPV was prepared and was used to biopan a 12-mer phage peptide library three times. The selected phage clones were identified by ELISA and then sequencing. The amino acid sequences detected by phage display were analyzed, and a mimic immuno-dominant epitope was identified. The epitope of VP1 is located in the N-terminal and contains the role amino acid sequence R-K-R. Immunization of mice indicated that the phage-displayed peptide induces antibodies against PPV. This study shows that peptide mimotopes have potential as alternatives to the complex antigens currently used for diagnosis of PPV infection or for development of vaccines

Thermal management of the hotspots in 3-D integrated circuits

Vertical integration for microelectronics possesses significant challenges due to its fast dissipation of heat generated in multiple device planes. This paper focuses on thermal management of a 3-D integrated circuit, and micro-channel cooling is adopted to deal with the 3-D integrated circuitthermal problems. In addition, thermal through-silicon vias are also used to improve the capacity of heat trans-mission. It is found that combination of microchannel cooling and thermal through-silicon vias can remarkably alleviate the hotspots. The results presented in this paper are expected to aid in the development of thermal design guidelines for 3-D integrated circuits

Monopole-charged pulsars and relevant issues

The aligned pulsars whose rotation axes and magnetic dipole axes are parallel should be positively charged. The total charge of pulsars is calculated after considering the electromagnetic field in and out the star under a specific condition. The statistical relation between the pulsar's rotation energy loss rate (or the period derivative) and the period may hint that the millisecond radio pulsars with small periods could be low-mass bare strange stars.Comment: 4 pages, 1 figures, and 1 tabl

DIGAP - a Database of Improved Gene Annotation for Phytopathogens

<p>Abstract</p> <p>Background</p> <p>Bacterial plant pathogens are very harmful to their host plants, which can cause devastating agricultural losses in the world. With the development of microbial genome sequencing, many strains of phytopathogens have been sequenced. However, some misannotations exist in these phytopathogen genomes. Our objective is to improve these annotations and store them in a central database DIGAP.</p> <p>Description</p> <p>DIGAP includes the following improved information on phytopathogen genomes. (i) All the 'hypothetical proteins' were checked, and non-coding ORFs recognized by the Z curve method were removed. (ii) The translation initiation sites (TISs) of 20% ~ 25% of all the protein-coding genes have been corrected based on the NCBI RefSeq, ProTISA database and an <it>ab initio </it>program, GS-Finder. (iii) Potential functions of about 10% 'hypothetical proteins' have been predicted using sequence alignment tools. (iv) Two theoretical gene expression indices, the codon adaptation index (CAI) and the <it>E</it>(<it>g</it>) index, were calculated to predict the gene expression levels. (v) Potential agricultural bactericide targets and their homology-modeled 3D structures are provided in the database, which is of significance for agricultural antibiotic discovery.</p> <p>Conclusion</p> <p>The results in DIGAP provide useful information for understanding the pathogenetic mechanisms of phytopathogens and for finding agricultural bactericides. DIGAP is freely available at <url>http://ibi.hzau.edu.cn/digap/</url>.</p

Latent profile analysis of psychological needs thwarting in Chinese school teachers: longitudinal associations with problematic smartphone use, psychological distress, and perceived administrative support

IntroductionIn light of the significant impact that teachers have on education quality and student growth, their mental health warrants special attention. With the increasing popularity of Information and Communication Technology (ICT) and the rise of online teaching during the pandemic, teachers have become a group prone to developing problematic smartphone use (PSU). Psychological need thwarting (PNT) has been shown to be closely related to PSU, psychological distress, and perceived administrative support. However, most previous studies have adopted a variable-centered approach, which may overlook the possibility that the three basic needs are not closely associated and could form distinct profiles. Therefore, this study aims to apply latent profile analysis to identify different PNT profiles and their associations with PSU, psychological distress, and perceived administrative support.MethodsA longitudinal survey was conducted using convenience and purposive sampling methods. The survey involved 1,642 primary and middle school teachers working in China over a two-month interval, with the first assessment in November 2021 (Time 1) and the second in January 2022 (Time 2).ResultsThe results indicate that a three-profile model, intricately based on the PNT data gathered at Time 1, is most optimal: Class 1 is labeled as ‘High autonomy-High competence and Moderate relatedness thwarting’, Class 2 as ‘High autonomy-High competence and High relatedness thwarting’, and Class 3 as ‘Low psychological needs thwarting’. Distinct associations were observed among the three profiles concerning PSU, psychological distress, and perceived administrative support. Specifically, in terms of PSU, the score of Class 2 was higher than Class 1, with that of Class 3 being the lowest at Time 1, while at Time 2 no significant difference was found between any two of these three groups; in terms of distress, the scores of the three profiles were arranged from high to low as Class 2, 1, and 3 at both time points; and in terms of perceived administrative support, the order was just the opposite, with 3, 1, and 2 from high to low at both Time 1 and Time 2.ConclusionNotably, the consistent ranking of the three classes in terms of psychological distress and administrative support suggests a lasting influence of PNT. Future studies should explore this enduring impact further by employing additional longitudinal data sets and examining potential mediators or moderators beyond the current study’s scope

Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization

Out-of-distribution (OOD) graph generalization are critical for many real-world applications. Existing methods neglect to discard spurious or noisy features of inputs, which are irrelevant to the label. Besides, they mainly conduct instance-level class-invariant graph learning and fail to utilize the structural class relationships between graph instances. In this work, we endeavor to address these issues in a unified framework, dubbed Individual and Structural Graph Information Bottlenecks (IS-GIB). To remove class spurious feature caused by distribution shifts, we propose Individual Graph Information Bottleneck (I-GIB) which discards irrelevant information by minimizing the mutual information between the input graph and its embeddings. To leverage the structural intra- and inter-domain correlations, we propose Structural Graph Information Bottleneck (S-GIB). Specifically for a batch of graphs with multiple domains, S-GIB first computes the pair-wise input-input, embedding-embedding, and label-label correlations. Then it minimizes the mutual information between input graph and embedding pairs while maximizing the mutual information between embedding and label pairs. The critical insight of S-GIB is to simultaneously discard spurious features and learn invariant features from a high-order perspective by maintaining class relationships under multiple distributional shifts. Notably, we unify the proposed I-GIB and S-GIB to form our complementary framework IS-GIB. Extensive experiments conducted on both node- and graph-level tasks consistently demonstrate the superior generalization ability of IS-GIB. The code is available at https://github.com/YangLing0818/GraphOOD.Comment: Accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE

Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training

Generating images from graph-structured inputs, such as scene graphs, is uniquely challenging due to the difficulty of aligning nodes and connections in graphs with objects and their relations in images. Most existing methods address this challenge by using scene layouts, which are image-like representations of scene graphs designed to capture the coarse structures of scene images. Because scene layouts are manually crafted, the alignment with images may not be fully optimized, causing suboptimal compliance between the generated images and the original scene graphs. To tackle this issue, we propose to learn scene graph embeddings by directly optimizing their alignment with images. Specifically, we pre-train an encoder to extract both global and local information from scene graphs that are predictive of the corresponding images, relying on two loss functions: masked autoencoding loss and contrastive loss. The former trains embeddings by reconstructing randomly masked image regions, while the latter trains embeddings to discriminate between compliant and non-compliant images according to the scene graph. Given these embeddings, we build a latent diffusion model to generate images from scene graphs. The resulting method, called SGDiff, allows for the semantic manipulation of generated images by modifying scene graph nodes and connections. On the Visual Genome and COCO-Stuff datasets, we demonstrate that SGDiff outperforms state-of-the-art methods, as measured by both the Inception Score and Fr\'echet Inception Distance (FID) metrics. We will release our source code and trained models at https://github.com/YangLing0818/SGDiff.Comment: Code and models shall be released at https://github.com/YangLing0818/SGDif