Vanillic Acid,a Bioactive Phenolic Compound,Counteracts LPS-Induced Neurotoxicity by Regulating c-Jun N-Terminal Kinase in Mouse Brain

The receptor for advanced glycation end products (RAGE), a pattern recognition receptor signaling event, has been associated with several human illnesses, including neurodegenerative diseases, particularly in Alzheimer’s disease (AD). Vanillic acid (V.A), a flavoring agent, is a benzoic acid derivative having a broad range of biological activities, including antioxidant, anti-inflammatory, and neuroprotective effects. However, the underlying molecular mechanisms of V.A in exerting neuroprotection are not well investigated. The present study aims to explore the neuroprotective effects of V.A against lipopolysaccharides (LPS)-induced neuroinflammation, amyloidogenesis, synaptic/memory dysfunction, and neurodegeneration in mice brain. Behavioral tests and biochemical and immunofluorescence assays were applied. Our results indicated increased expression of RAGE and its downstream phospho-c-Jun n-terminal kinase (p-JNK) in the LPS-alone treated group, which was significantly reduced in the V.A + LPS co-treated group. We also found that systemic administration of LPS-injection induced glial cells (microglia and astrocytes) activation and significantly increased expression level of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-KB) and secretion of proinflammatory cytokines including tumor necrosis factor alpha (TNF-α), interleukin-1 β (IL1-β), and cyclooxygenase (COX-2). However, V.A + LPS co-treatment significantly inhibited the LPS-induced activation of glial cells and neuroinflammatory mediators. Moreover, we also noted that V.A treatment significantly attenuated LPS-induced increases in the expression of AD markers, such as β-site amyloid precursor protein (APP)–cleaving enzyme 1 (BACE1) and amyloid-β (Aβ). Furthermore, V.A treatment significantly reversed LPS-induced synaptic loss via enhancing the expression level of pre- and post-synaptic markers (PSD-95 and SYP), and improved memory performance in LPS-alone treated group. Taken together; we suggest that neuroprotective effects of V.A against LPS-induced neurotoxicity might be via inhibition of LPS/RAGE mediated JNK signaling pathway; and encourage future studies that V.A would be a potential neuroprotective and neurotherapeutic candidate in various neurological disorders.     

Understanding the charge transfer and defect states in ZnO/In2O3 composite nanostructures (CNs)

Journal of Materials Science: Materials in Electronics - This report presents the VLS synthesis and the study of the charge carrier transport through defect states across the interface in ZnO/In2O3...     

Forecasting Groundwater Levels using a Hybrid of Support Vector Regression and Particle Swarm Optimization

Water Resources Management - Forecasting the groundwater level is crucial to managing water resources supply sustainably. In this study, a simulation–optimization hybrid model was developed...     

Recurrent type-1 fuzzy functions approach for time series forecasting

Forecasting the future values of a time series is a common research topic and is studied using probabilistic and non-probabilistic methods. For probabilistic methods, the autoregressive integrated moving average and exponential smoothing methods are commonly used, whereas for non-probabilistic methods, artificial neural networks and fuzzy inference systems (FIS) are commonly used. There are numerous FIS methods. While most of these methods are rule-based, there are a few methods that do not require rules, such as the type-1 fuzzy function (T1FF) approach. While it is possible to encounter a method such as an autoregressive (AR) model integrated with a T1FF, no method that includes T1FF and the moving average (MA) model in one algorithm has yet been proposed. The aim of this study is to improve forecasting by taking the disturbance terms into account. The input dataset is organized using the following variables. First, the lagged values of the time series are used for the AR model. Second, a fuzzy c-means clustering algorithm is used to cluster the inputs. Third, for the MA, the residuals of fuzzy functions are used. Hence, AR, MA, and the degree of memberships of the objects are included in the input dataset. Because the objective function is not derivative, particle swarm optimization is preferable for solving it. The results on several datasets show that the proposed method outperforms most of the methods in literature.     

Impairment of IGF-1 Signaling and Antioxidant Response Are Associated with Radiation Sensitivity and Mortality

Following exposure to high doses of ionizing radiation, diverse strains of vertebrate species will manifest varying levels of radiation sensitivity. To understand the inter-strain cellular and molecular mechanisms of radiation sensitivity, two mouse strains with varying radiosensitivity (C3H/HeN, and CD2F1), were exposed to total body irradiation (TBI). Since Insulin-like Growth Factor-1 (IGF-1) signaling pathway is associated with radiosensitivity, we investigated the link between systemic or tissue-specific IGF-1 signaling and radiosensitivity. Adult male C3H/HeN and CD2F1 mice were irradiated using gamma photons at Lethal Dose-70/30 (LD_70/30), 7.8 and 9.35 Gy doses, respectively. Those mice that survived up to 30 days post-irradiation, were termed the survivors. Mice that were euthanized prior to 30 days post-irradiation due to deteriorated health were termed decedents. The analysis of non-irradiated and irradiated survivor and decedent mice showed that inter-strain radiosensitivity and post-irradiation survival outcomes are associated with activation status of tissue and systemic IGF-1 signaling, nuclear factor erythroid 2–related factor 2 (Nrf2) activation, and the gene expression profile of cardiac mitochondrial energy metabolism pathways. Our findings link radiosensitivity with dysregulation of IGF-1 signaling, and highlight the role of antioxidant gene response and mitochondrial function in radiation sensitivity.     

Principal component analysis and correlation studies of spring wheats in relation to cookie making quality

Quality of cookies is profoundly influenced by the physicochemical and rheological properties of wheat grains. Therefore, it is pivotal to explore right choice of wheat cultivar. Current study was designed to gauge the relationship between wheat grain physiognomies and flour rheological behaviour with cookie characteristics. The outcomes depicted that selected wheat varieties varied significantly (p < 0.01) in various parameters like thousand kernel weight, test weight, pearling value, Pelshenke and zeleny value. In correlation, particle size index correlated negatively (r = ?0.67) with protein content and positively with water absorption. Spread factor of cookies was influenced by particle size index (r = ?0.63), Pelshenke (r = ?0.62), water absorption (r = ?0.60), and mixographic peak height (r = 0.85). Principal component analysis illustrated that thousand kernel weight, grain length, and width were major components in determining the water absorption of wheat flour. However, spread factor of cookies was partly depicted from Pelshenke value and partly from particle size index.     

Effect of in-service strengthening on the axial load carrying capacity of steel box columns

To avoid any interruption of service and considering economic issues, strengthening of existing columns usually occurs while the member is under service loads. One of the important issues being neglected in the redesign process of retrofitted columns is the effects of significant axial load existing in the columns. This paper presents a numerical study to investigate the behavior and the ultimate bearing capacity of box shaped steel columns reinforced with continuous welded plates while under load. In the first phase of the study, it is intended to evaluate the variation of results with respect to the existing design relations. For this purpose, the ultimate bearing capacity of un-preloaded models is assumed to be in accordance with the selected design code and the corresponding geometric imperfection for each model is defined using several analyses to reach the values obtained with the design code, for further studies. In the second phase, the magnitude of initial imperfection is set to 1/500 of the studied columns. In both phases, the effect of magnitude of existing preload on ultimate bearing capacity is investigated considering two influential parameters, namely the slenderness ratio of the column and the ratio between cross sectional area of the column and reinforcing plates. Results show that by increasing the preload magnitude, the ultimate bearing capacity of reinforced columns with identical reinforcing plates, decreases. This reduction is more notable for the columns with median slenderness ratios. However, as the magnitude of preload increases, the effect of slenderness ratio on the reduction of ultimate bearing capacity becomes more evident. Also, it is found that by using a fixed imperfection ratio of L/500, even for un-preloaded models the ultimate bearing capacity of the strengthened column will be less than the values calculated as per the selected design code. Finally, an empirical relation is proposed to calculate reduction of ultimate bearing capacity for columns affected from various preload level considering different slenderness ratios.     

On an Optimized Fuzzy Supervized Multiphase Guidance Law

Proportional navigation (PN) is an effective guidance law in chasing a constant speed target. However, intercepting a maneuvering target requires extra provisions to contain target acceleration. This is observed in the next generation of guidance laws, namely augmented proportional navigation (APN) and the optimal guidance law (OGL). The other advanced guidance method is integral sliding mode (ISM), which exhibits superb low miss distance (MD), but unfortunately at the cost of disappointing effective lateral acceleration (C_eff), higher than the modest level that APN and OGL demand. Reducing both MD and C_eff can be achieved using a multiphase algorithm. A setup of APN, OGL and ISM is proposed to integrate the strength of each and overcome their weaknesses. Hybrid algorithm smooth management is conducted by an optimally tuned fuzzy supervisory controller. The results indicate that in facing a constant acceleration target, an APN‐ISM twin yields the best results, while in case of a constant jerk target an APN‐OGL‐ISM triplet renders excellent interception. Miss distance as low as 0.012m can be achieved with significantly lower control effort than required by ISM alone. The simulations results confirm the conclusions and illustrate the capacity of the algorithm in combating maneuvering targets.