Dynamic Mathematics for Automated Machine Learning Techniques

Machine Learning and Neural Networks have been gaining popularity and are widely considered as the driving force of the Fourth Industrial Revolution. However, modern machine learning techniques such as backpropagation training was firmly established in 1986 while computer vision was revolutionised in 2012 with the introduction of AlexNet. Given all these accomplishments, why are neural networks still not an integral part of our society? ``Because they are difficult to implement in practice.'' I'd like to use machine learning, but I can't invest much time. The concept of Automated Machine Learning (AutoML) was first proposed by Professor Frank Hutter of the University of Freiburg. Machine learning is not simple; it requires a practitioner to have thorough understanding on the attributes of their data and the components which their model entails. AutoML is the effort to automate all tedious aspects of machine learning to form a clean data analysis pipeline. This thesis is our effort to develop and to understand ways to automate machine learning. Specifically, we focused on Recurrent Neural Networks (RNNs), Meta-Learning, and Continual Learning. We studied continual learning to enable a network to sequentially acquire skills in a dynamic environment; we studied meta-learning to understand how a network can be configured efficiently; and we studied RNNs to understand the consequences of consecutive actions. Our RNN-study focused on mathematical interpretability. We described a large variety of RNNs as one mathematical class to understand their core network mechanism. This enabled us to extend meta-learning beyond network configuration for network pruning and continual learning. This also provided insights for us to understand how a single network should be consecutively configured and led us to the creation of a simple generic patch that is compatible to several existing continual learning archetypes. This patch enhanced the robustness of continual learning techniques and allowed them to generalise data better. By and large, this thesis presented a series of extensions to enable AutoML to be made simple, efficient, and robust. More importantly, all of our methods are motivated with mathematical understandings through the lens of dynamical systems. Thus, we also increased the interpretability of AutoML concepts

Stock Market Simulation

Profitable investing in stock markets requires both in-depth knowledge of market dynamics and careful execution of trading strategies. Participants in this study investigated both the history and the workings of modern stock exchanges. Members also performed an eight-week simulation in which they employed distinct approaches to stock trading. The outcomes of this study show that during a recession, it is more profitable to invest in corporations whose stocks experience minimal volatility. Moreover, the results indicate that particular industries, such as defense contracting, outperform others in the current economic climate

Metabolic Profiling Reveals Biochemical Pathways Responsible for Eelgrass Response to Elevated CO\u3csub\u3e2\u3c/sub\u3e and Temperature

As CO2 levels in Earth’s atmosphere and oceans steadily rise, varying organismal responses may produce ecological losers and winners. Increased ocean CO2 can enhance seagrass productivity and thermal tolerance, providing some compensation for climate warming. However, the metabolic shifts driving the positive response to elevated CO2 by these important ecosystem engineers remain unknown. We analyzed whole-plant performance and metabolic profiles of two geographically distinct eelgrass (Zostera marina L.) populations in response to CO2 enrichment. In addition to enhancing overall plant size, growth and survival, CO2 enrichment increased the abundance of Calvin Cycle and nitrogen assimilation metabolites while suppressing the abundance of stress-related metabolites. Overall metabolome differences between populations suggest that some eelgrass phenotypes may be better suited than others to cope with an increasingly hot and sour sea. Our results suggest that seagrass populations will respond variably, but overall positively, to increasing CO2 concentrations, generating negative feedbacks to climate change

Profiling Protein Sâ Sulfination with Maleimideâ Linked Probes

Cysteine residues are susceptible to oxidation to form Sâ sulfinyl (Râ SO2H) and Sâ sulfonyl (Râ SO3H) postâ translational modifications. Here we present a simple bioconjugation strategy to label Sâ sulfinated proteins by using reporterâ linked maleimides. After alkylation of free thiols with iodoacetamide, Sâ sulfinated cysteines react with maleimide to form a sulfone Michael adduct that remains stable under acidic conditions. Using this sequential alkylation strategy, we demonstrate differential Sâ sulfination across mouse tissue homogenates, as well as enhanced Sâ sulfination following pharmacological induction of endoplasmic reticulum stress, lipopolysaccharide stimulation, and inhibitors of the electron transport chain. Overall, this study reveals a broadened profile of maleimide reactivity across cysteine modifications, and outlines a simple method for profiling the physiological role of cysteine Sâ sulfination in disease.Maleimide, but not iodoacetamide, reacts with aryl and alkyl sulfinic acid standards and Sâ sulfinated proteins to give a sulfonylâ succinimide adduct that is stable under acidic conditions. This sequential alkylation strategy can be used for selective sulfinic acid labeling in biological samples. This study reveals a broadened profile of maleimide reactivity across cysteine modifications in proteins.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138861/1/cbic201700137_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138861/2/cbic201700137.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138861/3/cbic201700137-sup-0001-misc_information.pd

Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIV

Clinical data usually cannot be freely distributed due to their highly confidential nature and this hampers the development of machine learning in the healthcare domain. One way to mitigate this problem is by generating realistic synthetic datasets using generative adversarial networks (GANs). However, GANs are known to suffer from mode collapse thus creating outputs of low diversity. This lowers the quality of the synthetic healthcare data, and may cause it to omit patients of minority demographics or neglect less common clinical practices. In this paper, we extend the classic GAN setup with an additional variational autoencoder (VAE) and include an external memory to replay latent features observed from the real samples to the GAN generator. Using antiretroviral therapy for human immunodeficiency virus (ART for HIV) as a case study, we show that our extended setup overcomes mode collapse and generates a synthetic dataset that accurately describes severely imbalanced class distributions commonly found in real-world clinical variables. In addition, we demonstrate that our synthetic dataset is associated with a very low patient disclosure risk, and that it retains a high level of utility from the ground truth dataset to support the development of downstream machine learning algorithms.Comment: In the near future, we will make our codes and synthetic datasets publicly available to facilitate future research. Follow us on https://healthgym.ai

RNA helicase Belle/DDX3 regulates transgene expression in Drosophila

Belle (Bel), the Drosophila homolog of the yeast DEAD-box RNA helicase DED1 and human DDX3, has been shown to be required for oogenesis and female fertility. Here we report a novel role of Bel in regulating the expression of transgenes. Abrogation of Bel by mutations or RNAi induces silencing of a variety of P-element-derived transgenes. This silencing effect depends on downregulation of their RNA levels. Our genetic studies have revealed that the RNA helicase Spindle-E (Spn-E), a nuage RNA helicase that plays a crucial role in regulating RNA processing and PIWI-interacting RNA (piRNA) biogenesis in germline cells, is required for loss-of-bel-induced transgene silencing. Conversely, Bel abrogation alleviates the nuage-protein mislocalization phenotype in spn-E mutants, suggesting a competitive relationship between these two RNA helicases. Additionally, disruption of the chromatin remodeling factor Mod(mdg4) or the microRNA biogenesis enzyme Dcr-1 also rescued the transgene-silencing phenotypes in bel mutants, suggesting the involvement of chromatin remodeling and microRNA biogenesis in loss-of-bel-induced transgene silencing. Finally we showed that genetic inhibition of Bel function led to the de novo generation of piRNAs from the transgene region inserted in the genome, suggesting a potential piRNA-dependent mechanism that may mediate transgene silencing as Bel function is inhibited. Our findings have demonstrated a novel involvement of Bel in regulating transgene expression and its loss triggers a transgene silencing mechanism mediated by protein regulators implicated in RNA processing, piRNA biogenesis, chromatin remodeling and the microRNA pathway

Tumor tissue-specific biomarkers of colorectal cancer by anatomic location and stage

The progress in the discovery and validation of metabolite biomarkers for the detection of colorectal cancer (CRC) has been hampered by the lack of reproducibility between study cohorts. The majority of discovery-phase biomarker studies have used patient blood samples to identify disease-related metabolites, but this pre-validation phase is confounded by non-specific disease influences on the metabolome. We therefore propose that metabolite biomarker discovery would have greater success and higher reproducibility for CRC if the discovery phase was conducted in tumor tissues, to find metabolites that have higher specificity to the metabolic consequences of the disease, that are then validated in blood samples. This would thereby eliminate any non-tumor and/or body response effects to the disease. In this study, we performed comprehensive untargeted metabolomics analyses on normal (adjacent) colon and tumor tissues from CRC patients, revealing tumor tissue-specific biomarkers (n = 39/group). We identified 28 highly discriminatory tumor tissue metabolite biomarkers of CRC by orthogonal partial least-squares discriminant analysis (OPLS-DA) and univariate analyses (VIP > 1.5, p 0.96, using various models. We further identified five biomarkers that were specific to the anatomic location of tumors in the colon (n = 236). The combination of these five metabolites (S-adenosyl-L-homocysteine, formylmethionine, fucose 1-phosphate, lactate, and phenylalanine) demonstrated high differentiative capability for left- and right-sided colon cancers at stage I by internal cross-validation (AUC = 0.804, 95% confidence interval, CI 0.670–0.940). This study thus revealed nine discriminatory biomarkers of CRC that are now poised for external validation in a future independent cohort of samples. We also discovered a discrete metabolic signature to determine the anatomic location of the tumor at the earliest stage, thus potentially providing clinicians a means to identify individuals that could be triaged for additional screening regimens

Microrheology with optical tweezers: data analysis

We present a data analysis procedure that provides the solution to a long-standing issue in microrheology studies, i.e. the evaluation of the fluids' linear viscoelastic properties from the analysis of a finite set of experimental data, describing (for instance) the time-dependent mean-square displacement of suspended probe particles experiencing Brownian fluctuations. We report, for the first time in the literature, the linear viscoelastic response of an optically trapped bead suspended in a Newtonian fluid, over the entire range of experimentally accessible frequencies. The general validity of the proposed method makes it transferable to the majority of microrheology and rheology techniques