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Aim and Background: Amylloid fibrils are filamentus protein aggregates derived from various proteins and peptides. They can be distinguished from other type of features according to their appear shape and electron microscope images, also by dye binding methods, which can indicate induced cross beta structures. Amyloid fibrils are correlated to creating general disease, amyloidosis. Disease such Alzheimer, Parkinson, diabetes type II, and others disease which in each of them, the special kind of protein subjected to form amyloid or amyloid like fibrils. A variety of proteins which they are not converted to amyloid fibrils invivo, can be transform to amyloids in special unstabilizing conditions.
Materials and Methods: Congored spectrophotometric method, ThT fluorescence and CD Data was used for fibril formation assay and Transmission Electron microscopy was used for final affirmation of fibrils.
Results: results shows that maximum amyloid formation was in 5 mg.ml-1 protein concentration, 50 ºC and 7.4 buffer pH.

Conclusion:
With the new approach obtained from the kappa casein, amyloid fibers can be introduced as new nanomaterials, Thus the results, given the diverse applications of nanomaterials, can affirm process optimization of amyloid production from accessible and inexpensive protein in milk.

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Aims: Considered as one of the marine resources and due to their effective compounds, cyanobacteria activate the cell death process in cancer cells and, thus, may be used as a new source. The aim of the current research was to evaluate the effect of oscillatoria cyanobacterium extract on breast cancer cell line and NM23 gene expression.
Materials and Methods: In the present experimental study, oscillatoria cyanobacterium was cultured in a negative zayander medium at 26°C to 28°C with a light intensity of 350 to 3500lux, under 12-hour lighting and 12-hour darkness, and the MCF-7 cell line was prepared. Breast cancer cells were treated by hydroalcoholic extracts of oscillatoria with different concentrations. The effect of extract on cell survival was evaluated by MTT assay and the effect of the extract on the changes of NM23 gene expression was investigated by Real-Time PCR.
Findings: The morphology of MCF-7 cell line showed that the oscillatoria cyanobacterium extract significantly altered the treated cells compared with control cells. The survival of cells decreased with increasing concentration, and there was a significant difference compared to the control sample. After 24 hours, the extract inhibited 50% cell survival at a concentration of 0.6mg/ml (p<0.001). The NM23 gene expression significantly increased over a 24-hour period compared with the control sample.

Conclusion: Oscillatoria Cyanobacterium extract decreases the breast cancer cell line and increases the NM23 gene expression.

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During a survey in the spring of 2024, noticeable severe leaf yellow spots, yellowing, fruit necrosis, and chlorotic ring spots symptoms were observed in Cucumber plants cultivated near Isfahan City, central Iran, to assess Orthotospovirus infection. Total genomic RNA was individually extracted from the leaves of 5 symptomatic and one asymptomatic leaf sample. The extracted RNA samples were subsequently subjected to RT-PCR using an Orthotospovirus-universal primer pair. An amplicon of the expected size was obtained with all diseased samples, and a BLAST search of its nucleotide sequences exhibited a high level of nucleotide identity with several MYSP isolates. This is the first report of the melon yellow spot virus (MYSV) Orthotospovirus meloflavi occurrence in Iran.



 

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The purpose of this paper is to propose an integrated model of clustering, AHP and Kano approaches. Based on customer segmentation and value achievement of each segment, the new model is expected to recommend appropriate service for each segment. Statistical population of this research includes customers of Saman Bank of Qom. After random sampling, 144 questionnaires have been used for data analysis. After data collection, the clustering approah has been used and clusters have been prioritized by the AHP approach and finally, the needs of each cluster have been determined using with the Kano methodology and appropriate service has been recommended for each cluster. The number of clusters has been addressed as four and the clusters have been prioritzed as the second, the third, the first and the fourth cluster. In the first cluster, customers' needs are distinguished as more, one-dimensional, attractive and indifferent; in the second and third clusters as more must-be; and in the fourth cluster as more one-dimensional. The results imply that the integratoin of the three approaches forms an empowered technique by which, an organization can achieve competitive advantage through market segmentation, valueable customer recognition/satisfaction

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The significant role of institutional conditions created by government and institutions involvement in the process of achieving sustainable development has attracted attention of policy makers to institutional innovation as one of the fundamental concepts in development strategies. On the other hand, achieving sustainable development requires access to technology and making appropriate policies for technology and innovation. This study seeks to denote the relationship between institutional innovation, technology development and the achievement of sustainable development using a partial least squares (PLS) model. The proposed model applying a questionnaire distributed among science and technology experts was estimated. The results show that institutional innovations as soft technologies will play great roles in achieving sustainable development and technology development. In addition, among technology innovations, strategizing and policy-making with a coefficient of 0.92 is of greatest importance in attaining sustainable development. As well, among technology development evidences, patent and copyright, technology management and technology environment with coefficients of 0.904, 0.89 and .0898 respectively have the highest significance in achieving sustainable development.

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In this paper, a vibration-based damage detection approach for multi-layered woven glass laminate using time signal processing and Neural Network (NN) is presented. In order to reduce noise in the experimental extracted signals, wavelet-based denoising has been applied. After data mining and feature extraction from processed signals, NN as a classifier is employed to detect the damaged GFRP. Different NN structures were tested in order to enhance the damage detection performance to recognize the most remarkable performance. Also, the performance of the presented method was evaluated when different mother of wavelets at different decomposition levels denoise signals so that the best signal processing method is selected. The results demonstrate the effect of NN structure on the damage detection technique, which in this research the best NN performance was obtained with 75 hidden layers and allocating 80%, 10% and 10% of data to training, evaluation and testing, respectively. Furthermore, denoising using db3 and bior3.7 mother wavelets at 2nd decomposition level leads to the highest accuracy as well as suitable calculation time compared to other mother wavelets. The proposed method based on real data at the data acquisition points detects damage in composite laminate with high accuracy at reasonable calculation time, hence it can be used for condition monitoring of composite laminate either offline or online, provided that adding online data acquisition equipment.