Reducing Catastrophic Forgetting in Self-Organizing Maps

An agent that is capable of continual or lifelong learning is able to continuously learn from potentially infinite streams of pattern sensory data. One major historic difficulty in building agents capable of such learning is that neural systems struggle to retain previously-acquired knowledge when learning from new data samples. This problem is known as catastrophic forgetting and remains an unsolved problem in the domain of machine learning to this day. To overcome catastrophic forgetting, different approaches have been proposed. One major line of thought advocates the use of memory buffers to store data where the stored data is then used to randomly retrain the model to improve memory retention. However, storing and giving access to previous physical data points results in a variety of practical difficulties particularly with respect to growing memory storage costs. In this work, we propose an alternative way to tackle the problem of catastrophic forgetting, inspired by and building on top of a classical neural model, the self-organizing map (SOM) which is a form of unsupervised clustering. Although the SOM has the potential to combat forgetting through the use of pattern-specializing units, we uncover that it too suffers from the same problem and this forgetting becomes worse when the SOM is trained in a task incremental fashion. To mitigate this, we propose a generalization of the SOM, the continual SOM (c-SOM), which introduces several novel mechanisms to improve its memory retention -- new decay functions and generative resampling schemes to facilitate generative replay in the model. We perform extensive experiments using split-MNIST with these approaches, demonstrating that the c-SOM significantly improves over the classical SOM. Additionally, we come up with a new performance metric alpha_mem to measure the efficacy of SOMs trained in a task incremental fashion, providing a benchmark for other competitive learning models

Neuro-mimetic Task-free Unsupervised Online Learning with Continual Self-Organizing Maps

An intelligent system capable of continual learning is one that can process and extract knowledge from potentially infinitely long streams of pattern vectors. The major challenge that makes crafting such a system difficult is known as catastrophic forgetting - an agent, such as one based on artificial neural networks (ANNs), struggles to retain previously acquired knowledge when learning from new samples. Furthermore, ensuring that knowledge is preserved for previous tasks becomes more challenging when input is not supplemented with task boundary information. Although forgetting in the context of ANNs has been studied extensively, there still exists far less work investigating it in terms of unsupervised architectures such as the venerable self-organizing map (SOM), a neural model often used in clustering and dimensionality reduction. While the internal mechanisms of SOMs could, in principle, yield sparse representations that improve memory retention, we observe that, when a fixed-size SOM processes continuous data streams, it experiences concept drift. In light of this, we propose a generalization of the SOM, the continual SOM (CSOM), which is capable of online unsupervised learning under a low memory budget. Our results, on benchmarks including MNIST, Kuzushiji-MNIST, and Fashion-MNIST, show almost a two times increase in accuracy, and CIFAR-10 demonstrates a state-of-the-art result when tested on (online) unsupervised class incremental learning setting

The analog of cGAMP, c-di-AMP, activates STING mediated cell death pathway in estrogen-receptor negative breast cancer cells

Immune adaptor protein like STING/MITA regulate innate immune response and plays a critical role in inflammation in the tumor microenvironment and regulation of metastasis including breast cancer. Chromosomal instability in highly metastatic cells releases fragmented chromosomal parts in the cytoplasm, hence the activation of STING via an increased level of cyclic dinucleotides (cDNs) synthesized by cGMP-AMP synthase (cGAS). Cyclic dinucleotides 2’ 3’-cGAMP and it's analog can potentially activate STING mediated pathways leading to nuclear translocation of p65 and IRF-3 and transcription of inflammatory genes. The differential modulation of STING pathway via 2’ 3’-cGAMP and its analog and its implication in breast tumorigenesis is still not well explored. In the current study, we demonstrated that c-di-AMP can activate type-1 IFN response in ER negative breast cancer cell lines which correlate with STING expression. c-di-AMP binds to STING and activates downstream IFN pathways in STING positive metastatic MDA-MB-231/MX-1 cells. Prolonged treatment of c-di-AMP induces cell death in STING positive metastatic MDA-MB-231/MX-1 cells mediated by IRF-3. c-di-AMP induces IRF-3 translocation to mitochondria and initiates Caspase-9 mediated cell death and inhibits clonogenicity of triple-negative breast cancer cells. This study suggests that c-di-AMP can activate and modulates STING pathway to induce mitochondrial mediated apoptosis in estrogen-receptor negative breast cancer cells

Arachidonic acid has a dominant effect to regulate lipogenic genes in 3T3-L1 adipocytes compared to omega-3 fatty acids

Background: The effects of long-chain n-3 and n-6 polyunsaturated fatty acids (PUFA) on the regulation of adipocytes metabolism are well known. These fatty acids are generally consumed together in our diets; however, the metabolic regulation of adipocytes in the presence of these fatty acids when given together is not known. Objective: To investigate the effects of n-3 PUFA and arachidonic acid (AA), an n-6 PUFA, on the regulation of adipogenic and lipogenic genes in mature 3T3-L1 adipocytes. Methods: 3T3-L1 adipocytes were incubated in the presence or absence of 100 µM of eicosapentaenoic acid, EPA; docosahexaenoic acid, DHA; docosapentaenoic acid, DPA and AA, either alone or AA+n-3 PUFA; control cells received bovine serum albumin alone. The mRNA expression of adipogenic and lipogenic genes was measured. The fatty acid composition of adipocytes was analyzed using gas chromatography. Results: Individual n-3 PUFA or AA had no effect on the mRNA expression of peroxisome-proliferator-activated receptor-γ; however, AA+EPA and AA+DPA significantly increased (P<0.05) the expression compared to control cells (38 and 42%, respectively). AA and AA+EPA increased the mRNA expression of acetyl-CoA carboxylase 1 (P<0.05). AA treatment decreased the mRNA expression of stearoyl-CoA desaturase (SCD1) (P<0.01), while n-3 PUFA, except EPA, had no effect compared to control cells. AA+DHA and AA+DPA inhibited SCD1 gene expression (P<0.05) suggesting a dominant effect of AA. Fatty acids analysis of adipocytes revealed a higher accretion of AA compared to n-3 PUFA. Conclusions: Our findings reveal that AA has a dominant effect on the regulation of lipogenic genes in adipocytes

Reducing Catastrophic Forgetting in Self Organizing Maps with Internally-Induced Generative Replay (Student Abstract)

A lifelong learning agent is able to continually learn from potentially infinite streams of pattern sensory data. One major historic difficulty in building agents that adapt in this way is that neural systems struggle to retain previously-acquired knowledge when learning from new samples. This problem is known as catastrophic forgetting (interference) and remains an unsolved problem in the domain of machine learning to this day. While forgetting in the context of feedforward networks has been examined extensively over the decades, far less has been done in the context of alternative architectures such as the venerable self-organizing map (SOM), an unsupervised neural model that is often used in tasks such as clustering and dimensionality reduction. Although the competition among its internal neurons might carry the potential to improve memory retention, we observe that a fixed-sized SOM trained on task incremental data, i.e., it receives data points related to specific classes at certain temporal increments, it experiences severe interference. In this study, we propose the c-SOM, a model that is capable of reducing its own forgetting when processing information

Emerging trends in tuberculosis therapy—A review

191-208Tuberculosis is one of the most devastating bacterial disease having high rates of morbidity and mortality. Mycobacterium tuberculosis invades the host immune system and persists in pulmonary granulomas. Review presents bacterial cell wall or envelope structure, drug targets against M. tuberculosis and novel therapeutics agents like quinolone, pyrimidine, nitroimidazopyran and oxazolidinone derivatives. This review also focuses on the newer untouched targets like immunomodulators and isocitrate lyase inhibitors, which can be further exploited

Shrimp Oil Extracted from Shrimp Processing By-Product Is a Rich Source of Omega-3 Fatty Acids and Astaxanthin-Esters, and Reveals Potential Anti-Adipogenic Effects in 3T3-L1 Adipocytes

The province of Newfoundland and Labrador, Canada, generates tons of shrimp processing by-product every year. Shrimp contains omega (n)-3 polyunsaturated fatty acids (PUFA) and astaxanthin (Astx), a potent antioxidant that exists in either free or esterified form (Astx-E). In this study, shrimp oil (SO) was extracted from the shrimp processing by-product using the Soxhlet method (hexane:acetone 2:3). The extracted SO was rich in phospholipids, n-3 PUFA, and Astx-E. The 3T3-L1 preadipocytes were differentiated to mature adipocytes in the presence or absence of various treatments for 8 days. The effects of SO were then investigated on fat accumulation, and the mRNA expression of genes involved in adipogenesis and lipogenesis in 3T3-L1 cells. The effects of fish oil (FO), in combination with Astx-E, on fat accumulation, and the mRNA expression of genes involved in adipogenesis and lipogenesis were also investigated. The SO decreased fat accumulation, compared to untreated cells, which coincided with lower mRNA expression of adipogenic and lipogenic genes. However, FO and FO + Astx-E increased fat accumulation, along with increased mRNA expression of adipogenic and lipogenic genes, and glucose transporter type 4 (Glut-4), compared to untreated cells. These findings have demonstrated that the SO is a rich source of n-3 PUFA and Astx-E, and has the potential to elicit anti-adipogenic effects. Moreover, the SO and FO appear to regulate adipogenesis and lipogenesis via independent pathways in 3T3-L1 cells