The Effect of Capsaicin on IGF-I and IGF-IR Expression in Ovarian Granulosa Cells

ΔΕΝ ΔΙΑΤΙΘΕΤΑΙ ΠΕΡΙΛΗΨΗCapsaicin (trans-8-methyl-N-vanillyl-6-noneadamide) is a pungent ingredient in red peppers from the Capsicum family. Insulin-like growth factor-I (IGF-I) is expressed in granulosa cells and has an important role in ovarian development. However, there are no data about the IGF-I expression in ovarian granulosa cells after capsaicin treatment. The aim of this study was to investigate the expression of IGF-I and its receptor (insulin-like growth factor-I receptor [IGF-IR]) in primary rat ovarian granulosa cells after low and high doses of capsaicin treatment. For this, granulosa cells were isolated and cultured from ovaries of 30-day-old female Sprague-Dawley rats. Granulosa cell plates were divided into four groups as cell control (C), vehicle control (V), and 50 μM and 150 μM capsaicin groups. In experimental groups, granulosa cells were exposed to capsaicin for 24 hours and immunocytochemistry was performed afterwards using anti-IGF-I and anti-IGF-IR antibodies. Both IGF-I and IGF-IR expressions were found to be significantly increased in parallel to the capsaicin doses. Elevated levels of IGF-I may be a risk factor for ovarian development. Because of the crucial role of IGF-I in ovary development, capsaicin treatment can be effective on follicular development and/or disorders characterized by high IGF-I levels

Predicting Shear Capacity of RC Beams Strengthened with NSM FRP Using Neural Networks

This research aims to predict the shear capacity of NSM FRP beams using the neural network method. The study investigates the key considerations and the necessary analysis for this prediction. NSM FRP beams are reinforced concrete beams that are strengthened with near-surface mounted (NSM) fiber-reinforced polymer (FRP) composites. Accurately predicting their shear capacity is important for ensuring their safety and reliability in real-world applications. The neural network method is a machine learning approach that is increasingly used in engineering analysis and design. The study explores how this method can be used to predict the shear capacity of NSM FRP beams and what factors should be taken into account in this analysis. The research also discusses the analytical approach required for this prediction, highlighting the necessary steps for obtaining accurate results. Overall, this study provides valuable insights into the use of the neural network method for predicting the shear capacity of NSM FRP beams. The findings can help inform future research and practical applications in the field of structural engineering, contributing to the development of safer and more reliable structures

Production of high temperature-resistant strains of Agaricus bitorquis

In this study, the culture mushroom Agaricus bitorquis (Quel.) Sacc. was examined for growth of mycelia and fructifications under high temperature. The spores taken from the mushrooms that were collected from nature were grouped as A, B, C, D and E. These spores were inoculated into malt extract agar and incubated at 30ºC and primer mycelium was produced. The mycelium discus taken from primer mycelium in 8 mm diameter were inoculated into the center of malt extract agar and incubated at 30ºC, 32ºC, 34ºC, 36ºC, and 38ºC, separately. During the incubation period the growth of the mycelia were measured. The best mycelia growth for all groups was seen at 30ºC. At 36ºC, the E group mycelia and at 38ºC other group’s mycelia did not grow. These temperatures were determined as thermal lethal point for the groups. From all the mycelia produced spawn was prepared and inoculated into compost and incubated at 30ºC and 32ºC. The harvested mushrooms were inspected morphologically.Key words: Agaricus bitorquis, mycelial growth, high temperature

Hybrid hidden Markov LSTM for short-term traffic flow prediction

Deep learning (DL) methods have outperformed parametric models such as historical average, ARIMA and variants in predicting traffic variables into short and near-short future, that are critical for traffic management. Specifically, recurrent neural network (RNN) and its variants (e.g. long short-term memory) are designed to retain long-term temporal correlations and therefore are suitable for modeling sequences. However, multi-regime models assume the traffic system to evolve through multiple states (say, free-flow, congestion in traffic) with distinct characteristics, and hence, separate models are trained to characterize the traffic dynamics within each regime. For instance, Markov-switching models with a hidden Markov model (HMM) for regime identification is capable of capturing complex dynamic patterns and non-stationarity. Interestingly, both HMM and LSTM can be used for modeling an observation sequence from a set of latent or, hidden state variables. In LSTM, the latent variable is computed in a deterministic manner from the current observation and the previous latent variable, while, in HMM, the set of latent variables is a Markov chain. Inspired by research in natural language processing, a hybrid hidden Markov-LSTM model that is capable of learning complementary features in traffic data is proposed for traffic flow prediction. Results indicate significant performance gains in using hybrid architecture compared to conventional methods such as Markov switching ARIMA and LSTM

High precision determination of the Q2Q^2-evolution of the Bjorken Sum

We present a significantly improved determination of the Bjorken Sum for 0.6$\leq Q^{2}\leq$4.8 GeV$^{2}$ using precise new $g_{1}^{p}$ and $g_{1}^{d}$ data taken with the CLAS detector at Jefferson Lab. A higher-twist analysis of the $Q^{2}$-dependence of the Bjorken Sum yields the twist-4 coefficient $f_{2}^{p-n}=-0.064 \pm0.009\pm_{0.036}^{0.032}$. This leads to the color polarizabilities $\chi_{E}^{p-n}=-0.032\pm0.024$ and $\chi_{B}^{p-n}=0.032\pm0.013$. The strong force coupling is determined to be \alpha_{s}^{\overline{\mbox{ MS}}}(M_{Z}^{2})=0.1124\pm0.0061, which has an uncertainty a factor of 1.5 smaller than earlier estimates using polarized DIS data. This improvement makes the comparison between $\alpha_{s}$ extracted from polarized DIS and other techniques a valuable test of QCD.Comment: Published in Phys. Rev. D. V1: 8 pages, 3 figures. V2: Updated references; Included threshold matching in \alpha_s evolution. Corrected a typo on the uncertainty for \Lambda_QCD. V3: Published versio

A note on the effect of pre-slaughter transport duration on nutrient composition and fatty acid profile of broiler breast meat.

WOS: 00029493340000

A note on the effect of pre-slaughter transport duration on nutrient composition and fatty acid profile of broiler breast meat

WOS: 000294933400008The aim of this study was to investigate the influence of pre-slaughter transport duration, as a stress factor, on nutrient and fatty acid composition of broiler breast meat. The study was conducted on 48 breast muscles obtained from Ross broilers slaughtered at the average weight of 1.8 or 2.6 kg, 36 and 46 days old, respectively. Transport duration was 1.5 or 3 h. Heavier broilers transported for longer duration had the higher protein content, while lighter broilers gave similar results. Lower moisture but higher lipid content was observed for the long transported broilers. Fatty acid composition was significantly influenced by body weight and transport duration: a lower content of PUFA was obtained for heavier broilers. Ratios of PUFA/SFA was lower in heavier broilers. A decrease in MUFA and a lower n-3/n-6 ratio was evidenced in the meat from broilers transported for longer duration.Abalioglu Yem; Soya ve Tekstil Sanayi A.SSupported by Abalioglu Yem, Soya ve Tekstil Sanayi A.

A Bayesian approach to quantifying uncertainties and improving generalizability in traffic prediction models

Deep-learning models for traffic data prediction can have superior performance in modeling complex functions using a multi-layer architecture. However, a major drawback of these approaches is that most of these approaches do not offer forecasts with uncertainty estimates, which are essential for traffic operations and control. Without uncertainty estimates, it is difficult to place any level of trust to the model predictions, and operational strategies relying on overconfident predictions can lead to worsening traffic conditions. In this study, we propose a Bayesian recurrent neural network framework for uncertainty quantification in traffic prediction with higher generalizability by introducing spectral normalization to its hidden layers. In our paper, we have shown that normalization alters the training process of deep neural networks by controlling the model's complexity and reducing the risk of overfitting to the training data. This, in turn, helps improve the generalization performance of the model on out-of-distribution datasets. Results demonstrate that spectral normalization improves uncertainty estimates and significantly outperforms both the layer normalization and model without normalization in single-step prediction horizons. This improved performance can be attributed to the ability of spectral normalization to better localize the feature space of the data under perturbations. Our findings are especially relevant to traffic management applications, where predicting traffic conditions across multiple locations is the goal, but the availability of training data from multiple locations is limited. Spectral normalization, therefore, provides a more generalizable approach that can effectively capture the underlying patterns in traffic data without requiring location-specific models

Glycosaminoglycan mimetric peptide nanofibers promote mineralization by osteogenic cells

Cataloged from PDF version of article.Bone tissue regeneration is accomplished by concerted regulation of protein-based extracellular matrix components, glycosaminoglycans (GAGs) and inductive growth factors. GAGs constitute a significant portion of the extracellular matrix and have a significant impact on regulating cellular behavior, either directly or through encapsulation and presentation of growth factors to the cells. In this study we utilized a supramolecular peptide nanofiber system that can emulate both the nanofibrous architecture of collagenous extracellular matrix and the major chemical composition found on GAGs. GAGs and collagen mimetic peptide nanofibers were designed and synthesized with sulfonate and carboxylate groups on the peptide scaffold. The GAG mimetic peptide nanofibers interact with bone morphogenetic protein-2 (BMP-2), which is a critical growth factor for osteogenic activity. The GAG mimicking ability of the peptide nanofibers and their interaction with BMP-2 promoted osteogenic activity and mineralization by osteoblastic cells. Alkaline phosphatase activity, Alizarin red staining and energy dispersive X-ray analysis spectroscopy indicated the efficacy of the peptide nanofibers in inducing mineralization. The multifunctional and bioactive microenvironment presented here provides osteoblastic cells with osteogenic stimuli similar to those observed in native bone tissue