Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly

Change detection in multispectral images based on fusion of change vector analysis in posterior probability space and posterior probability space angle mapper

Change vector analysis in posterior probability space (CVAPS) has been introduced recently as an effective method for change detection. CVAPS is based on the length of change and its direction in a posterior probability (PP) space. However, CVAPS is prone to similar direction cosine values. An approach to analyzing change by combining CVAPS and a new method called posterior probability space angle mapper (PSAM) is proposed in this study. PSAM establishes the similarity between two PP vectors of a pixel for two different dates by calculating the angle between them. This research presents a new change-detection algorithm based on combining CVAPS and PSAM (CVAPSAM), which is able to fully exploit change vectors in a PP space. While CVAPS uses a suitable threshold value to detect changes, CVAPSAM does not need to set a threshold. In addition, it reduces the similar direction cosine values source of error in identifying ‘from-to’ classes

Production of Al2O3–SiC nano-composites by spark plasma sintering

In this paper, Al2O3–SiC composites were produced by SPS at temperatures of 1600 °C for 10 min under vacuum atmosphere. For preparing samples, Al2O3 with the second phase including of micro and nano-sized SiC powder were milled for 5 h. The milled powders were sintered in a SPS machine. After sintering process, phase studies, densification and mechanical properties of Al2O3–SiC composites were examined. Results showed that the specimens containing micro-sized SiC have an important effect on bulk density, hardness and strength. The highest relative density, hardness and strength were 99.7%, 324.6 HV and 2329 MPa, respectively, in Al2O3–20 wt% SiCmicro composite. Due to short time sintering, the growth was limited and grains still remained in nano-meter scale

Fusion of Change Vector Analysis in Posterior Probability Space and Postclassification Comparison for Change Detection from Multispectral Remote Sensing Data

Postclassification Comparison (PCC) has been widely used as a change-detection method. The PCC algorithm is straightforward and easily applicable to all satellite images, regardless of whether they are acquired from the same sensor or in the same environmental conditions. However, PCC is prone to cumulative error, which results from classification errors. Alternatively, Change Vector Analysis in Posterior Probability Space (CVAPS), which interprets change based on comparing the posterior probability vectors of a pixel, can alleviate the classification error accumulation present in PCC. CVAPS identifies the type of change based on the direction of a change vector. However, a change vector can be translated to a new position within the feature space; consequently, it is not inconceivable that identical measures of direction may be used by CVAPS to describe multiple types of change. Our proposed method identifies land-cover transitions by using a fusion of CVAPS and PCC. In the proposed algorithm, contrary to CVAPS, a threshold does not need to be specified in order to extract change. Moreover, the proposed method uses a Random Forest as a trainable fusion method in order to obtain a change map directly in a feature space which is obtained from CVAPS and PCC. In other words, there is no need to specify a threshold to obtain a change map through the CVAPS method and then combine it with the change map obtained from the PCC method. This is an advantage over other change-detection methods focused on fusing multiple change-detection approaches. In addition, the proposed method identifies different types of land-cover transitions, based on the fusion of CVAPS and PCC, to improve the results of change-type determination. The proposed method is applied to images acquired by Landsat and Quickbird. The resultant maps confirm the utility of the proposed method as a change-detection/labeling tool. For example, the new method has an overall accuracy and a kappa coefficient relative improvement of 7% and 9%, respectively, on average, over CVAPS and PCC in determining different types of change

Dairy market selection approach using MCDM methods ::a case of Iranian dairy market

This paper addresses the development of a multi-criteria decision making (MCDM) procedure including a pattern extraction from success and failure of current market introduction to generate a logical framework for target market selection. In addition of covering the gap of comprehensive research in Iran's domestic dairy market, using combined MCDM methods and grey systems, the main problem this paper tries to solve is the selection of a target market which covers not only the limitations of the company's current markets but also possesses their advantages. A real-world implementation of the approach is done within a dairy company located in Iran. Considering Tehran, the North, Khuzestan, and West Azerbaijan as the current market, and also Mashhad, Isfahan, Shiraz, and Tabriz as the potential market, Shiraz selected as the best target market through analysis of various criteria. Results show that considering current markets has a big influence on the market ranking procedure