Early-age hydration studies of Portland cement

Our current knowledge on cement hydration during setting is based on the discrete observation of hydrated paste. An advanced micro/nano-level technique which can perform the in-situ observation on the continuous hydration of cement paste is demanded. In this study, Raman spectroscopy (RS) was chosen as such a method to continuously investigate wet pastes. The objective of this research is to explore the hydration process and microstructural development of fresh pastes with this technique. This research was conducted in three phases. First, the RS analysis was used to continuously observe the cement hydration from 20 minutes after mixing to 9 hours. Based on the analysis, it was found that RS was able to characterize both the cement ingredient (C3S, C2S, C3A, and gypsum) and hydration products (C-S-H gel, CH, ettringite, and monosulfate). The evolution of these components during the setting period was also detected by RS. The obtained Raman signals can indicate the end of the dormant period and initial setting in the cement paste. The RS was then expanded to study the differences in the hydration mechanisms of hardened ordinary cement paste and ultra-high performance concrete (UHPC). The silica fume included into the UHPC was found to react quickly during the first two weeks. The content changes of calcium silicates, calcium hydroxide (CH), and ettringite were different in the ordinary paste and UHPC. In the third phase, Raman chemical mapping was implemented on the hardened cement paste to explore the microstructure developments during the hydration process. A mapping protocol was developed. The obtained maps were able to correctly reflect the distributions and connections of the different paste components. From the mapping study on the cement paste, it was found that ettringite tended to locate on the surface of calcium silicates, while CH was apt to concentrate and localize in the pores as big crystals

β-n-oxalyl-l-α, β -diaminopropionic acid (β -odap) content in lathyrus sativus: The integration of nitrogen and sulfur metabolism through β -cyanoalanine synthase

Grass pea (Lathyrus sativus L.) is an important legume crop grown mainly in South Asia and Sub-Saharan Africa. This underutilized legume can withstand harsh environmental conditions including drought and flooding. During drought-induced famines, this protein-rich legume serves as a food source for poor farmers when other crops fail under harsh environmental conditions; however, its use is limited because of the presence of an endogenous neurotoxic nonprotein amino acid β-N-oxalyl-l-α,β-diaminopropionic acid (β-ODAP). Long-term consumption of Lathyrus and β-ODAP is linked to lathyrism, which is a degenerative motor neuron syndrome. Pharmacological studies indicate that nutritional deficiencies in methionine and cysteine may aggravate the neurotoxicity of β-ODAP. The biosynthetic pathway leading to the production of β-ODAP is poorly understood, but is linked to sulfur metabolism. To date, only a limited number of studies have been conducted in grass pea on the sulfur assimilatory enzymes and how these enzymes regulate the biosynthesis of β-ODAP. Here, we review the current knowledge on the role of sulfur metabolism in grass pea and its contribution to β-ODAP biosynthesis. Unraveling the fundamental steps and regulation of β-ODAP biosynthesis in grass pea will be vital for the development of improved varieties of this underutilized legume

A Literature Review of the Influence of Social Capital on Commercial Credit

As a typical informal system, social capital plays an important supplementary role in China’s economic transition period. Informal finance based on commercial credit plays a role in the economic cycle. However, there is currently a lack of literature to directly examine the impact of social capital on the use of commercial credit by enterprises. This article aims to systematically sort out the theoretical development of social capital on commercial credit, which mainly includes the definition of social capital, its effects, the influencing factors of commercial credit, and the summary of the existing research results of social capital on commercial credit. It is hoped that this literature review will provide guidance for future research

Differentially Private Diffusion Auction: The Single-unit Case

Diffusion auction refers to an emerging paradigm of online marketplace where an auctioneer utilises a social network to attract potential buyers. Diffusion auction poses significant privacy risks. From the auction outcome, it is possible to infer hidden, and potentially sensitive, preferences of buyers. To mitigate such risks, we initiate the study of differential privacy (DP) in diffusion auction mechanisms. DP is a well-established notion of privacy that protects a system against inference attacks. Achieving DP in diffusion auctions is non-trivial as the well-designed auction rules are required to incentivise the buyers to truthfully report their neighbourhood. We study the single-unit case and design two differentially private diffusion mechanisms (DPDMs): recursive DPDM and layered DPDM. We prove that these mechanisms guarantee differential privacy, incentive compatibility and individual rationality for both valuations and neighbourhood. We then empirically compare their performance on real and synthetic datasets

Multiple linear epitopes (B-cell, CTL and Th) of JEV expressed in recombinant MVA as multiple epitope vaccine induces a protective immune response

Epitope-based vaccination might play an important role in the protective immunity against Japanese encephalitis virus (JEV) infection. The purpose of the study is to evaluate the immune characteristics of recombinant MVA carrying multi-epitope gene of JEV (rMVA-mep). The synthetic gene containing critical epitopes (B-cell, CTL and Th) of JEV was cloned into the eukaryotic expression vector pGEM-K1L, and the rMVA-mep was prepared. BALB/c mice were immunized with different dosages of purified rMVA-mep and the immune responses were determined in the form of protective response against JEV, antibodies titers (IgG1 and IgG2a), spleen cell lymphocyte proliferation, and the levels of interferon-γ and interleukin-4 cytokines. The results showed that live rMVA-mep elicited strongly immune responses in dose-dependent manner, and the highest level of immune responses was observed from the groups immunized with 107 TCID50 rMVA-mep among the experimental three concentrations. There were almost no difference of cytokines and neutralizing antibody titers among 107 TCID50 rMVA-mep, recombinant ED3 and inactivated JEV vaccine. It was noteworthy that rMVA-mep vaccination potentiates the Th1 and Th2-type immune responses in dose-dependent manner, and was sufficient to protect the mice survival against lethal JEV challenge. These findings demonstrated that rMVA-mep can produce adequate humoral and cellular immune responses, and protection in mice, which suggested that rMVA-mep might be an attractive candidate vaccine for preventing JEV infection

Response of Soil Fungal Community Structure to Long-Term Continuous Soybean Cropping

Long-term continuous soybean cropping can lead to the aggravation of soil fungal disease. However, the manner in which the fungal community and functional groups of fungi are affected by continuous soybean cropping remains unclear. We investigated the fungal abundance, composition and diversity during soybean rotation (RS), 2-year (SS) and long-term (CS) continuous soybean cropping systems using quantitative real-time PCR and high-throughput sequencing. The results showed that the fungal abundance was significantly higher in CS than in SS and RS. CS altered the fungal composition. Compared with RS, SS had an increase of 29 and a decrease of 12 genera in fungal relative abundance, and CS increased 38 and decreased 17 genera. The Shannon index was significantly higher in CS and SS than in RS. The result of principal coordinate analysis (PCoA) showed that CS and SS grouped together and were clearly separated from RS on the PCoA1. A total of 32 features accounted for the differences in fungal composition across RS, SS, and CS. The relative abundance of 10 potentially pathogenic and 10 potentially beneficial fungi changed, and most of their relative abundances dramatically increased in SS and CS compared with RS. Our study indicated that CS results in selective stress on pathogenic and beneficial fungi and causes the development of the fungal community structure that is antagonistic to plant health

ECA-TFUnet: A U-shaped CNN-Transformer network with efficient channel attention for organ segmentation in anatomical sectional images of canines

Automated organ segmentation in anatomical sectional images of canines is crucial for clinical applications and the study of sectional anatomy. The manual delineation of organ boundaries by experts is a time-consuming and laborious task. However, semi-automatic segmentation methods have shown low segmentation accuracy. Deep learning-based CNN models lack the ability to establish long-range dependencies, leading to limited segmentation performance. Although Transformer-based models excel at establishing long-range dependencies, they face a limitation in capturing local detail information. To address these challenges, we propose a novel ECA-TFUnet model for organ segmentation in anatomical sectional images of canines. ECA-TFUnet model is a U-shaped CNN-Transformer network with Efficient Channel Attention, which fully combines the strengths of the Unet network and Transformer block. Specifically, The U-Net network is excellent at capturing detailed local information. The Transformer block is equipped in the first skip connection layer of the Unet network to effectively learn the global dependencies of different regions, which improves the representation ability of the model. Additionally, the Efficient Channel Attention Block is introduced to the Unet network to focus on more important channel information, further improving the robustness of the model. Furthermore, the mixed loss strategy is incorporated to alleviate the problem of class imbalance. Experimental results showed that the ECA-TFUnet model yielded 92.63% IoU, outperforming 11 state-of-the-art methods. To comprehensively evaluate the model performance, we also conducted experiments on a public dataset, which achieved 87.93% IoU, still superior to 11 state-of-the-art methods. Finally, we explored the use of a transfer learning strategy to provide good initialization parameters for the ECA-TFUnet model. We demonstrated that the ECA-TFUnet model exhibits superior segmentation performance on anatomical sectional images of canines, which has the potential for application in medical clinical diagnosis