Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

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

Speech enhancement using a reduced complexity MFCC-based Deep Neural Network

In contrast to classical noise reduction methods introduced over the past decades, this work focuses on a regression-based single-channel speech enhancement framework using DNN, as recently introduced by Liu et al.. While the latter framework can lead to improved speech quality compared to classical approaches, it is afflicted by high computational complexity in the training stage. The main contribution of this work is to reduce the DNN complexity by introducing a spectral feature mapping from noisy mel frequency cepstral coefficients (MFCC) to enhanced short time Fourier transform (STFT) spectrum. Leveraging MFCC not only has the advantage of mimicking the logarithmic perception of human auditory system, but this approach requires much fewer input features and consequently lead to reduced DNN complexity. Exploiting the frequency domain speech features obtained from the results of such a mapping also avoids the information loss in reconstructing the time-domain speech signal from its MFCC. While the proposed method aims to predict clean speech spectra from corrupted speech inputs, its performance is further improved by incorporating information about the noise environment into the training phase. We implemented the proposed DNN method with different numbers of MFCC and used it to enhance several different types of noisy speech files. Experimental results of perceptual evaluation of speech quality (PESQ) show that the proposed approach can outperform the benchmark algorithms including a recently proposed non-negative matrix factorization (NMF) approach, and this for various speakers and noise types, and different SNR levels. More importantly, the proposed approach with MFCC leads to a significant reduction in complexity, where the runtime is reduced by a factor of approximately five.Contrairement aux méthodes classiques de réduction du bruit introduites au cours des dernières décennies, ce travail se concentre sur un cadre d'application de réhaussement de la parole monocanal basé sur la régression au moyen d'un réseau de neurones profond (DNN), proposé par Liu et al.. Alors que ce nouveau cadre d'application peut conduire à une meilleure qualité de la parole par rapport aux approches classiques, il est caractérisé par une complexité de calcul élevée dans la tâche d'apprentissage. La principale contribution de ce travail est de réduire la complexité du DNN par modélisation de la transformation entre les coefficients cepstraux à fréquence mel (MFCC) du signal bruité et la transformée de Fourier à court terme du signal parole rehaussé. Le fait de tirer parti des MFCC a non seulement l'avantage d'imiter la perception logarithmique du système auditif humain, mais cette approche nécessite beaucoup moins de variables d'entrée, ce qui en retour réduit la complexité du DNN. Exploiter les caractéristiques spectrales de la parole obtenues au moyen de cette transformation évite également la perte d'information inhérente lors de la reconstruction du signal parole dans le domaine temporel à partir des MFCC.Alors que la méthode proposée viseà prédire des spectres de parole propres à partir d'entrées corrompues, ses performances sont encore amélioréesen intégrant des informations sur l'environnement de bruit dans la phase d'entraînement.Nous avons mis en oeuvre la méthode DNN proposée avec différents nombres de MFCC et l'avons appliquée au rehaussement de signaux de parole contaminés par différents types de bruit.Les résultats expérimentaux de l'évaluation perceptuelle de la qualité de la parole (PESQ) montrentque l'approche proposée surpasse les algorithmes de référence incluant un algorithme récent de factorisation matricielle non-negative (NMF), et ceci pour différents locuteurs, types de bruit, et niveaux de rapport signal-sur-bruit De manière plus importante, la nouvelle approche conduit à une réduction significative de la complexité de calcul et du temps d'éxécution, par un facteur d'environ cinq