STUDY ON REINFORCEMENT OF FABRICATED HOLLOW SLAB BRIDGE BY POLYURETHANE-CEMENT COMPOSITE (PUC)

In this paper, a new polyurethane-cement composite (PUC) material is used to reinforce a 25-year hollow slab bridge. PUC material is composed of polyurethane and cement, which has good mechanical properties. After pouring PUC material at the bottom of the hollow slabs, the traffic can be restored in a short time. Ultimate bearing capacity was discussed based on the concrete structures. The failure mode of the reinforced beam depends on the PUC material. The strengthening process includes surface treatment of concrete, formwork erection and polyurethane cement pouring. In order to verify the effectiveness of PUC reinforced bridges, load tests were carried out before and after reinforcement. The test results showed that PUC could remove the bridge load and increase the stiffness of the hollow slabs

Cellulase Recycling after High-Solids Simultaneous Saccharification and Fermentation of Combined Pretreated Corncob

Despite the advantageous prospect of second-generation bioethanol, its final commercialization must overcome the primary cost impediment due to enzyme assumption. To solve this problem, this work achieves high-concentration ethanol fermentation and multi-round cellulase recycling through process integration. The optimal time and temperature of the re-adsorption process were determined by monitoring the adsorption kinetics of cellulases. Both glucose and cellobiose inhibited cellulase adsorption. After 96 h of ethanol fermentation, 40% of the initial cellulase remained in the broth, from which 62.5% of the cellulase can be recycled and reused in fresh substrate re-adsorption for 90 min. Under optimum conditions, i.e., pH 5.0, dry matter loading of 15 wt%, cellulase loading of 45 FPU/g glucan, two cycles of fermentation and re-adsorption can yield two-fold increased ethanol outputs and reduce enzyme costs by over 50%. The ethanol concentration in each cycle can be achieved at levels greater than 40 g/L

Integration analysis of senescence-related genes to predict prognosis and immunotherapy response in soft-tissue sarcoma: evidence based on machine learning and experiments

Background: Soft tissue sarcoma (STS) is the malignancy that exhibits remarkable histologic diversity. The diagnosis and treatment of STS is currently challenging, resulting in a high lethality. Chronic inflammation has also been identified as a key characteristic of tumors, including sarcomas. Although senescence plays an important role in the progression of various tumors, its molecular profile remains unclear in STS.Methods: We identified the senescence-related genes (SRGs) in database and depicted characteristics of genomic and transcriptomic profiling using cohort within TCGA and GEO database. In order to investigate the expression of SRGs in different cellular subtypes, single-cell RNA sequencing data was applied. The qPCR and our own sequencing data were utilized for further validation. We used unsupervised consensus clustering analysis to establish senescence-related clusters and subtypes. A senescence scoring system was established by using principal component analysis (PCA). The evaluation of clinical and molecular characteristics was conducted among distinct groups.Results: These SRGs showed differences in SCNV, mutation and mRNA expression in STS tissues compared to normal tissues. Across several cancer types, certain shared features of SRGs were identified. Several SRGs closely correlated with immune cell infiltration. Four clusters related to senescence and three subtypes related to senescence, each with unique clinical and biological traits, were established. The senescence scoring system exhibited effectiveness in predicting outcomes, clinical traits, infiltrations of immune cells and immunotherapy responses.Conclusion: Overall, the current study provided a comprehensive review of molecular profiling for SRGs in STS. The SRGs based clustering and scoring model could help guiding the clinical management of STS

Deciphering the role of NETosis-related signatures in the prognosis and immunotherapy of soft-tissue sarcoma using machine learning

Background: Soft-tissue sarcomas (STSs) are a rare type of cancer, accounting for about 1% of all adult cancers. Treatments for STSs can be difficult to implement because of their diverse histological and molecular features, which lead to variations in tumor behavior and response to therapy. Despite the growing importance of NETosis in cancer diagnosis and treatment, researches on its role in STSs remain limited compared to other cancer types.Methods: The study thoroughly investigated NETosis-related genes (NRGs) in STSs using large cohorts from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and Support Vector Machine Recursive Feature Elimination (SVM-RFE) were employed for screening NRGs. Utilizing single-cell RNA-seq (scRNA-seq) dataset, we elucidated the expression profiles of NRGs within distinct cellular subpopulations. Several NRGs were validated by quantitative PCR (qPCR) and our proprietary sequencing data. To ascertain the impact of NRGs on the sarcoma phenotype, we conducted a series of in vitro experimental investigations. Employing unsupervised consensus clustering analysis, we established the NETosis clusters and respective NETosis subtypes. By analyzing DEGs between NETosis clusters, an NETosis scoring system was developed.Results: By comparing the outcomes obtained from LASSO regression analysis and SVM-RFE, 17 common NRGs were identified. The expression levels of the majority of NRGs exhibited notable dissimilarities between STS and normal tissues. The correlation with immune cell infiltration were demonstrated by the network comprising 17 NRGs. Patients within various NETosis clusters and subtypes exhibited different clinical and biological features. The prognostic and immune cell infiltration predictive capabilities of the scoring system were deemed efficient. Furthermore, the scoring system demonstrated potential for predicting immunotherapy response.Conclusion: The current study presents a systematic analysis of NETosis-related gene patterns in STS. The results of our study highlight the critical role NRGs play in tumor biology and the potential for personalized therapeutic approaches through the application of the NETosis score model in STS patients

Manifold Path Guiding for Importance Sampling Specular Chains

Complex visual effects such as caustics are often produced by light paths containing multiple consecutive specular vertices (dubbed specular chains), which pose a challenge to unbiased estimation in Monte Carlo rendering. In this work, we study the light transport behavior within a sub-path that is comprised of a specular chain and two non-specular separators. We show that the specular manifolds formed by all the sub-paths could be exploited to provide coherence among sub-paths. By reconstructing continuous energy distributions from historical and coherent sub-paths, seed chains can be generated in the context of importance sampling and converge to admissible chains through manifold walks. We verify that importance sampling the seed chain in the continuous space reaches the goal of importance sampling the discrete admissible specular chain. Based on these observations and theoretical analyses, a progressive pipeline, manifold path guiding, is designed and implemented to importance sample challenging paths featuring long specular chains. To our best knowledge, this is the first general framework for importance sampling discrete specular chains in regular Monte Carlo rendering. Extensive experiments demonstrate that our method outperforms state-of-the-art unbiased solutions with up to 40x variance reduction, especially in typical scenes containing long specular chains and complex visibility.Comment: 14 pages, 19 figure

The effect of cage ventilation rate on the health of mice housed in Individually Ventilated Cages

The number of air changes per hour (ACH), an important index for individually ventilated cages (IVC), strongly affects the cage microenvironment and the health of laboratory animals. The objective of this study was to determine whether high or low cage ventilation adversely affects the health of mice housed in IVC systems and to identify cage ventilation rates suitable for the welfare of mice. We tested three different cage ventilation rates (40, 60, and 80 ACH) for 3 weeks in an IVC system. The temperature, relative humidity and ammonia concentrations in the cages were measured daily. The indices used to assess mouse health at specific time points throughout the study were body weight, stress hormones, T lymphocyte subsets (CD4 and CD8), immunoglobulins (IgG, IgM and IgA) and immune cells. There were no significant differences in body weight, growth hormones, immunoglobulin and T lymphocyte subsets in the IVC groups compared with the control group. The concentrations of corticosterone and epinephrine on day 7 of cage ventilation at 80 ACH were significantly higher than those in the control group (P < 0.05). Mice housed in 80 ACH cages had the lowest immune cell counts among all groups, and the numbers of lymphocytes and neutrophils were significantly lower than those in the control group (P < 0.05). In summary, cage ventilation at 60 ACH provided an optimum cage microenvironment for mouse health and welfare

Asiatic Acid Exhibits Anti-inflammatory and Antioxidant Activities against Lipopolysaccharide and d-Galactosamine-Induced Fulminant Hepatic Failure

Inflammation and oxidative stress are essential for the pathogenesis of fulminant hepatic failure (FHF). Asiatic acid (AA), which is a pentacyclic triterpene that widely occurs in various vegetables and fruits, has been reported to possess antioxidant and anti-inflammatory properties. In this study, we investigated the protective effects of AA against lipopolysaccharide (LPS) and d-galactosamine (GalN)-induced FHF and the underlying molecular mechanisms. Our findings suggested that AA treatment effectively protected against LPS/d-GalN-induced FHF by lessening the lethality; decreasing the alanine transaminase and aspartate aminotransferase levels, interleukin (IL)-1β, IL-6, and tumor necrosis factor-α production, malondialdehyde formation, myeloperoxidase level and reactive oxygen species generation (i.e., H2O2, NO, and O2−), and increasing the glutathione and superoxide dismutase contents. Moreover, AA treatment significantly inhibited mitogen-activated protein kinase (MAPK) and nuclear factor-kappa B (NF-κB) signaling pathway activation via the partial induction of programmed cell death 4 (PDCD4) protein expressions, which are involved in inflammatory responses. Furthermore, AA treatment dramatically induced the expression of the glutamate-cysteine ligase modifier subunit, the glutamate-cysteine ligase catalytic subunit, heme oxygenase-1, and NAD (P) H: quinoneoxidoreductase 1 (NQO1), which are largely dependent on activation of the nuclear factor-erythroid 2-related factor 2 (Nrf2) through the induction of AMP-activated protein kinase (AMPK) and glycogen synthase kinase-3β (GSK3β) phosphorylation. Accordingly, AA exhibited protective roles against LPS/d-GalN-induced FHF by inhibiting oxidative stress and inflammation. The underlying mechanism may be associated with the inhibition of MAPK and NF-κB activation via the partial induction of PDCD4 and upregulation of Nrf2 in an AMPK/GSK3β pathway activation-dependent manner