Action-vectors: Unsupervised movement modeling for action recognition

Representation and modelling of movements play a significant role in recognising actions in unconstrained videos. However, explicit segmentation and labelling of movements are non-trivial because of the variability associated with actors, camera viewpoints, duration etc. Therefore, we propose to train a GMM with a large number of components termed as a universal movement model (UMM). This UMM is trained using motion boundary histograms (MBH) which capture the motion trajectories associated with the movements across all possible actions. For a particular action video, the MAP adapted mean vectors of the UMM are concatenated to form a fixed dimensional representation referred to as 'super movement vector' (SMV). However, SMV is still high dimensional and hence, Baum-Welch statistics extracted from the UMM are used to arrive at a compact representation for each action video, which we refer to as an 'action-vector'. It is shown that even without the use of class labels, action-vectors provide a more discriminatory representation of action classes translating to a 8 % relative improvement in classification accuracy for action-vectors based on MBH features over naïve MBH features on the UCF101 dataset. Furthermore, action-vectors projected with LDA achieve 93% accuracy on the UCF101 dataset which rivals state-of-the-art deep learning techniques

Feature selection using Deep Neural Networks

Feature descriptors involved in video processing are generally high dimensional in nature. Even though the extracted features are high dimensional, many a times the task at hand depends only on a small subset of these features. For example, if two actions like running and walking have to be identified, extracting features related to the leg movement of the person is enough. Since, this subset is not known apriori, we tend to use all the features, irrespective of the complexity of the task at hand. Selecting task-aware features may not only improve the efficiency but also the accuracy of the system. In this work, we propose a supervised approach for task-aware selection of features using Deep Neural Networks (DNN) in the context of action recognition. The activation potentials contributed by each of the individual input dimensions at the first hidden layer are used for selecting the most appropriate features. The selected features are found to give better classification performance than the original high-dimensional features. It is also shown that the classification performance of the proposed feature selection technique is superior to the low-dimensional representation obtained by principal component analysis (PCA)

Klotho pathways, myelination disorders, neurodegenerative diseases, and epigenetic drugs

In this review we outline a rationale for identifying neuroprotectants aimed at inducing endogenous Klotho activity and expression, which is epigenetic action, by definition. Such an approach should promote remyelination and/or stimulate myelin repair by acting on mitochondrial function, thereby heralding a life-saving path forward for patients suffering from neuroinflammatory diseases. Disorders of myelin in the nervous system damage the transmission of signals, resulting in loss of vision, motion, sensation, and other functions depending on the affected nerves, currently with no effective treatment. Klotho genes and their single-pass transmembrane Klotho proteins are powerful governors of the threads of life and death, true to the origin of their name, Fates, in Greek mythology. Among its many important functions, Klotho is an obligatory co-receptor that binds, activates, and/or potentiates critical fibroblast growth factor activity. Since the discovery of Klotho a little over two decades ago, it has become ever more apparent that when Klotho pathways go awry, oxidative stress and mitochondrial dysfunction take over, and age-related chronic disorders are likely to follow. The physiological consequences can be wide ranging, potentially wreaking havoc on the brain, eye, kidney, muscle, and more. Central nervous system disorders, neurodegenerative in nature, and especially those affecting the myelin sheath, represent worthy targets for advancing therapies that act upon Klotho pathways. Current drugs for these diseases, even therapeutics that are disease modifying rather than treating only the symptoms, leave much room for improvement. It is thus no wonder that this topic has caught the attention of biomedical researchers around the world.https://www.liebertpub.com/doi/10.1089/biores.2020.0004Published versio

Pathogenic mitochondrial dysfunction and metabolic abnormalities

Herein we trace links between biochemical pathways, pathogenesis, and metabolic diseases to set the stage for new therapeutic advances. Cellular and acellular microorganisms including bacteria and viruses are primary pathogenic drivers that cause disease. Missing from this statement are subcellular compartments, importantly mitochondria, which can be pathogenic by themselves, also serving as key metabolic disease intermediaries. The breakdown of food molecules provides chemical energy to power cellular processes, with mitochondria as powerhouses and ATP as the principal energy carrying molecule. Most animal cell ATP is produced by mitochondrial synthase; its central role in metabolism has been known for >80 years. Metabolic disorders involving many organ systems are prevalent in all age groups. Progressive pathogenic mitochondrial dysfunction is a hallmark of genetic mitochondrial diseases, the most common phenotypic expression of inherited metabolic disorders. Confluent genetic, metabolic, and mitochondrial axes surface in diabetes, heart failure, neurodegenerative disease, and even in the ongoing coronavirus pandemic.https://doi.org/10.1016/j.bcp.2021.11480

Substrate Specifity Profiling of the Aspergillus fumigatus Proteolytic Secretome Reveals Consensus Motifs with Predominance of Ile/Leu and Phe/Tyr

The filamentous fungus Aspergillus fumigatus (AF) can cause devastating infections in immunocompromised individuals. Early diagnosis improves patient outcomes but remains challenging because of the limitations of current methods. To augment the clinician's toolkit for rapid diagnosis of AF infections, we are investigating AF secreted proteases as novel diagnostic targets. The AF genome encodes up to 100 secreted proteases, but fewer than 15 of these enzymes have been characterized thus far. Given the large number of proteases in the genome, studies focused on individual enzymes may overlook potential diagnostic biomarkers.As an alternative, we employed a combinatorial library of internally quenched fluorogenic probes (IQFPs) to profile the global proteolytic secretome of an AF clinical isolate in vitro. Comparative protease activity profiling revealed 212 substrate sequences that were cleaved by AF secreted proteases but not by normal human serum. A central finding was that isoleucine, leucine, phenylalanine, and tyrosine predominated at each of the three variable positions of the library (44.1%, 59.1%, and 57.0%, respectively) among substrate sequences cleaved by AF secreted proteases. In contrast, fewer than 10% of the residues at each position of cleaved sequences were cationic or anionic. Consensus substrate motifs were cleaved by thermostable serine proteases that retained activity up to 50°C. Precise proteolytic cleavage sites were reliably determined by a simple, rapid mass spectrometry-based method, revealing predominantly non-prime side specificity. A comparison of the secreted protease activities of three AF clinical isolates revealed consistent protease substrate specificity fingerprints. However, secreted proteases of A. flavus, A. nidulans, and A. terreus strains exhibited striking differences in their proteolytic signatures.This report provides proof-of-principle for the use of protease substrate specificity profiling to define the proteolytic secretome of Aspergillus fumigatus. Expansion of this technique to protease secretion during infection could lead to development of novel approaches to fungal diagnosis

Action Recognition Based on Discriminative Embedding of Actions Using Siamese Networks

Actions can be recognized effectively when the various atomic attributes forming the action are identified and combined in the form of a representation. In this paper, a low-dimensional representation is extracted from a pool of attributes learned in a universal Gaussian mixture model using factor analysis. However, such a representation cannot adequately discriminate between actions with similar attributes. Hence, we propose to classify such actions by leveraging the corresponding class labels. We train a Siamese deep neural network with a contrastive loss on the low-dimensional representation. We show that Siamese networks allow effective discrimination even between similar actions. The efficacy of the proposed approach is demonstrated on two benchmark action datasets, HMDB51 and MPII Cooking Activities. On both the datasets, the proposed method improves the state-of-the-art performance considerably

Unsupervised Universal Attribute Modelling for Action Recognition

A fixed dimensional representation for action clips of varying lengths has been proposed in the literature using aggregation models like bag-of-words and Fisher vector. These representations are high-dimensional and require classification techniques for action recognition. In this paper, we propose a framework for unsupervised extraction of a discriminative low-dimensional representation called action-vector. To start with, local spatio-temporal features are utilized to capture the action attributes implicitly in a large Gaussian mixture model called universal attribute model (UAM). To enhance the contribution of the significant attributes in each action clip, a maximum \textit{aposteriori} adaptation of the UAM means is performed for each clip. This results in a concatenated mean vector called super action vector (SAV) for each action clip. However, the SAV is still high-dimensional because of the presence of redundant attributes. Hence, we employ factor analysis to represent every SAV only in terms of the few important attributes contributing to the action clip. This leads to a low-dimensional representation called action-vector. This entire procedure requires no class labels and produces action-vectors that are distinct representations of each action irrespective of inter-actor variability encountered in unconstrained videos. An evaluation on trimmed action datasets UCF101 and HMDB51 demonstrates the efficacy of action-vectors for action classification over state-of-the-art techniques. Moreover, we also show that action-vectors can adequately represent untrimmed videos from the THUMOS14 dataset and produce classification results comparable to existing techniques