Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

BEHAVIOR OF CONCRETE WITH DIFFERENT PROPORTIONS OF NATURAL AND RECYCLED AGGREGATES

The concept of using recycled aggregate for making concrete is slowly but definitely gaining popularity. Research is in progress to make this technology feasible. The results of the present investigation, reported here, is one such attempt. The basic objective of the present study, is to attain acceptable quality of concrete with maximum utilisation of recycled aggregate in place of natural aggregate. In this paper, the term ''Replacement ratio'' is introduced and is defined as ratio of recycled coarse aggregate to total coarse aggregate in a concrete mix. Concrete mix cases with replacement ratio of 0, 0.25, 0.50, 0.75 and 1.00 have been considered for the present investigation, to evaluate its behaviour both in fresh and hardened states. The above study has been performed for three mix cases, with water-cement ratio of 0.57, 0.50 and 0.43 respectively. Empirical relations have been proposed, to estimate the moduli of elasticity and of rupture of concrete with any replacement ratio. A maximum value of the replacement ratio is suggested, keeping in view, the overall behaviour of concrete and the basic objective of this investigation

Feed-Forward Neural Network-Based Predictive Image Coding for Medical Image Compression

The generation of high volume of medical images in recent years has increased the demand for more efficient compression methods to cope up with the storage and transmission problems. In the case of medical images, it is important to ensure that the compression process does not affect the image quality adversely. In this paper, a predictive image coding method is proposed which preserves the quality of the medical image in the diagnostically important region (DIR) even after compression. In this method, the image is initially segmented into two portions, namely, DIR and non-DIR portions using a graph based segmentation procedure. The prediction process is implemented using two identical feed- forward neural networks (FF-NNs) at the compression and decompression stages. Gravitational search and particle swarm algorithms are used for training the FF-NNs. Prediction is performed both in a lossless (LLP) and near lossless (NLLP) manner for evaluating the performances of the two FF-NN training algorithms. The prediction error sequence which is the difference between the actual and predicted pixel values is further compressed using a Markov model based arithmetic coding. The proposed method is tested using CLEF med 2009 database. The experimental results demonstrate that the proposed method is equipped for compressing the medical images with minimum degradation in the image quality. It’s found that the gravitational search method achieves higher PSNR values compared to the particle swarm and backpropagation methods