Leukocytes as mediators of gut-brain communication

Food allergies ▪ Reactions range from mild/delayed to severe/rapid. ▪ People with mild allergic reactions have increased re-exposure risks. ▪ Cow’s milk allergy tends to manifest with milder allergic reactions. Cow’s milk allergy (CMA) ▪ CMA has been associated with behavioral and neurological disorders. ▪ How allergic inflammatory signals from the gut reach the brain is unclearhttps://commons.und.edu/bms-pp/1005/thumbnail.jp

Dietary whey protein increases brain leukocytes in mice regardless of their hypersensitivity status

Cow’s milk allergy (CMA) often manifests as milder reactions and may be linked to neurological problems. Previously, we demonstrated that C57BL/6J mice sensitized to a bovine whey allergen, β-lactoglobulin (BLG, Bos d 5), moderately increased BLG-specific IgE levels and exhibited behavioral changes without severe allergic reactions. When these non-anaphylactic CMA mice were placed on a whey-protein (WP)-containing diet for 2 weeks to simulate continuous dairy consumption, we found neuropathology indicative of neuroinflammation and cortical demyelination. Since immune cells migrate to the central nervous system (CNS) and promote neuroinflammation in demyelinating conditions such as multiple sclerosis, we hypothesized that the number of leukocytes would increase in BLG-sensitized mouse brains to orchestrate neuropathology. To test this hypothesis, we used flow cytometry to determine the number and phenotypes of leukocytes in the brains of naïve, sham, and BLG-sensitized mice after the 2 weeks of the WP diet. The frequencies of cells expressing common leukocyte marker CD45, pan T cell marker CD3, cytotoxic T cell marker CD8, integrin CD11b, myeloid cell marker CD14, and co-stimulatory marker CD86 significantly increased, regardless of the sensitization status. The percentages of these cells were low in mice that never received WP. This result indicated that WP diet consumption alone increased CNS leukocyte populations. Additional immunophenotyping is needed to determine whether the identified cells can be differentiated among the experimental groups. Detailed characterization of CNS leukocyte phenotypes and dynamics will help elucidate the mechanism of CMA-induced neuroinflammation and cortical demyelination.https://commons.und.edu/bms-pp/1001/thumbnail.jp

Milk allergen increases intestinal immune cells in association with neuroinflammation and behavioral changes

Cow’s milk allergy (CMA) CMA h as been associated with neurological disorders. How allergic inflammatory signals from the gut reach the brain is unclear.https://commons.und.edu/bms-pp/1006/thumbnail.jp

Optimal Placement and Sizing of DGs in Distribution Networks Using MLPSO Algorithm

In today’s world, distributed generation (DG) is an outstanding solution to tackle the challenges in power grids such as the power loss of the system that is intensified by the exponential increase in demand for electricity. Numerous optimization algorithms have been used by several researchers to establish the optimal placement and sizing of DGs to alleviate this power loss of the system. However, in terms of the reduction of active power loss, the performance of these algorithms is weaker. Furthermore, the premature convergence, the precision of the output, and the complexity are a few major drawbacks of these optimization techniques. Thus, this paper proposes the multileader particle swarm optimization (MLPSO) for the determination of the optimal locations and sizes of DGs with the objective of active power loss minimization while surmounting the drawbacks in previous algorithms. A comprehensive performance analysis is carried out utilizing the suggested approach on the standard IEEE 33 bus system and a real radial bus system in the Malaysian context. The findings reveal a 67.40% and an 80.32% reduction of losses in the two systems by integrating three DGs with a unity power factor, respectively. The comparison of the results with other optimization techniques demonstrated the effectiveness of the proposed MLPSO algorithm in optimal placement and sizing of DGs

Determinants of Non-performing Loans in Non-banking Financial Institutions: Evidence from Sri Lanka

This study attempts to investigate the factors affecting non-performing loans (NPLs) of non-bank financial institutions (NBFIs) in Sri Lanka by analyzing ten licensed finance and specialized leasing companies. The study employed a quantitative approach by using secondary data from corporate reports of the selected NBFIs for the period of 2011 to 2020. The sample of the study consist with five licensed finance companies and five specialized leasing companies which together claims for 65 percent of the NBFIs industry. The dependent variable under investigation is non-performing loans while independent variables include macro-economic and institutional-specific factors. The study used four proxies to measure institutional-specific factors; size of the company, return on assets, capital adequacy and efficiency. Considered macro-economic factors are GDP, inflation, and interest rates. The impact of macro-economic and institutional-specific factors on non-performing loans were observed through multiple regression model under ordinary least square method using EViews statistical software. The results of the model revealed that NPLs has a significant negative relationship with return on assets, GDP and inflation and a significant positive relationship with interest rates of the economy. Further, it was found that capital adequacy, efficiency and size factors do not have a significant relationship with NPLs. The findings highlighted the need for better credit risk mitigation practices in the non-banking sector to improve the stability of the financial system

The Optimal Placement and Sizing of Distributed Generation in an Active Distribution Network with Several Soft Open Points

A competent methodology based on the active power loss reduction for optimal placement and sizing of distributed generators (DGs) in an active distribution network (ADN) with several soft open points (SOPs) is proposed. A series of SOP combinations are explored to generate different network structures and they are utilized in the optimization framework to identify the possible solutions with minimum power loss under normal network conditions. Furthermore, a generalized methodology to optimize the size and the location of a predefined number of DGs with a predefined number of SOPs is presented. A case study on the modified IEEE 33 bus system with three DGs and five SOPs was conducted and hence the overall network power loss and the voltage improvement were examined. The findings reveal that the system loss of the passive network without SOPs and DGs is reduced by 79.5% using three DGs and five SOPs. In addition, this research work introduces a framework using the DG size and the impedance to the DG integration node, to propose a region where the DGs can be optimally integrated into an ADN that includes several SOPs

A complete state estimation algorithm for a three-phase four-wire low voltage distribution system with high penetration of solar PV

Low Voltage Distribution Grids (LVDGs) become highly unbalanced due to the advent of single-phase solar PV plants. As a result, the voltage and current levels of the neutral conductor show a significant increase. Therefore, monitoring of the entire state of the network is essential. However, the existing state estimation algorithms estimate voltage states of the phase conductors while ignoring the state of the neutral conductor. This paper presents a novel approach to estimate the complete state of the LVDGs. A novel state reduction method was introduced to model the three-phase four-wire feeder line using a admittance matrix, which incorporates the neutral coupling effect on phase conductors. Next, the reduced admittance matrix together with the linear approximations of active and reactive power functions were combined to formulate the Low Voltage-Linear State Estimation (LV-LSE) algorithm. Finally, the performance of LV-LSE algorithm was analyzed for different measurement uncertainties, scales of line lengths of the network, and data-loss conditions. Results show that, for all the cases, LV-LSE algorithm together with the proposed reduction method can estimate voltage states with an average maximum voltage magnitude error of less than pu and current states with an average maximum current magnitude error of less than pu

Generalized approach to assess and characterise the impact of solar PV on LV networks

Many of the studies which analyse the impact of solar photovoltaic (PV) on low voltage distribution networks (LVDNs) are based on sample networks or synthetic networks such as IEEE test cases. Therefore, the conclusions drawn in these studies are often specific to the study cases, limiting their applicability for generalization. This paper proposes a methodology that can generate a multitude of network topologies that have statistically similar characteristics to a selected cohort of existing networks. Furthermore, a stochastic evaluation based on the Monte Carlo technique is utilized to analyse the impacts of solar PV on generated LVDN models. A case study was conducted on ten networks that were generated statistically similar to existing urban networks to allow for a more generalized study. A total of 1000 Monte Carlo simulations for each network was carried out to accurately identify the most representative parameters that reflect the voltage rise and voltage unbalance in the considered LVDN cohort. Two parameters, namely: the momentum of the PV capacity and the mean absolute deviation, were identified from the case studies as the most representative parameters to analyse the impact of voltage rise and voltage unbalance factor. Thereafter a generalized framework based on these two parameters was derived to determine the impact of PV connections to the selected cohort of networks. This framework facilitates an efficient process for the utility supplier to determine the impact of incorporating new PV connections without the need for extensive studies