Distributed Medical Image Analysis and Diagnosis through Crowd-Sourced Games: A Malaria Case Study

In this work we investigate whether the innate visual recognition and learning capabilities of untrained humans can be used in conducting reliable microscopic analysis of biomedical samples toward diagnosis. For this purpose, we designed entertaining digital games that are interfaced with artificial learning and processing back-ends to demonstrate that in the case of binary medical diagnostics decisions (e.g., infected vs. uninfected), with the use of crowd-sourced games it is possible to approach the accuracy of medical experts in making such diagnoses. Specifically, using non-expert gamers we report diagnosis of malaria infected red blood cells with an accuracy that is within 1.25% of the diagnostics decisions made by a trained medical professional

Some Problems in Conic, Nonsmooth, and Online Optimization

Thesis (Ph.D.)--University of Washington, 2023In this thesis, we study algorithms with provable guarantees for structured optimization problemsarising in machine learning and theoretical computer science. One of the threads of this thesis is semidefinite programs (SDPs), a problem class with a variety of uses in engineering, computational mathematics, and computer science. Concretely, we study approximately solving the MaxCUT SDP. Aside from its significance as the SDP relaxation of an NP-hard problem, it has found use in matrix completion algorithms. Our algorithm for this problem combines ideas from the multiplicative weights framework and variance-reduced estimators. We adopt this idea of robust updates to give a faster high-accuracy algorithm to solve general SDPs via interior-point methods. Conceptually, our result for this general problem resolves the prior paradox of cutting-plane methods being faster at solving SDPs than interior-point methods, despite the former tracking far less structural information about the iterates. A common structural assumption on real-world datasets is that of sparsity or low rank. This structure is mathematically captured by convex non-smooth functions, thus making convex non- smooth optimization a cornerstone of signal processing and machine learning (e.g., in compressed sensing and low-rank matrix problems). Non-smooth optimization has risen in prominence on the non-convex front as well in the context of deep learning (e.g., in deep neural networks). In this thesis, we focus on two problems under the umbrella of nonsmooth optimization: In the2 convex setting, we study minimizing finite sum problems with each function depending only on a subset of the coordinates of the problem variable, and our proposed scheme develops a generalized cutting-plane framework; in the nonconvex setting, we focus on the problem of finding a Goldstein stationary point, and our solution combines randomization with geometric insights into prior work along with a novel application of cutting-plane methods. Optimization techniques have been used with great success to further progress in foundational questions in applied linear algebra. We explore this interplay in two questions. We first study least-squares regression with non-negative data and problem variables. This structure appears in several real-world datasets (e.g., in astronomy, text mining, and image processing) but has generally not been leveraged by standard least-squares algorithms (including ones in commercial software); in contrast, we utilize this structure, yielding improvements in the runtime (in both theory and experiments). We further study the computation of ℓ p Lewis weights. These are generalized importance scores of a given matrix used to sample a small number of key rows in tall data matrices and thus a crucial primitive in modern machine learning pipelines. We offer a fresh perspective to this problem, departing from the prior approach of using a fixed-point iteration. We also apply optimization theory in the context of market economics. Specifically, we study budget-constrained online advertising, an important problem for many technological companies, and develop an optimal-regret bidding algorithm under the “return-on-spend” constraint. Our main insight combines a novel white-box analysis of first-order methods for packing LPs with problem-specific structure

Glyoxalase pathway of trypanosomatid parasites: a promising chemotherapeutic target

Trypanosomatids are pathogenic protozoa of the order Kinetoplastida. A unique feature of these parasitic protozoa is the presence of a unique dithiol trypanothione (N1, N8 -bis(glutathionyl)spermidine) and the flavoenzyme trypanothione reductase. This is in contrast to human and other eukaryotes, which contain ubiquitous glutathione/glutathione reductase system. An important function of thiols is to protect cells from toxic metabolic by-products such as methylglyoxal, a reactive 2-oxoaldehyde. Methylglyoxal is a mutagenic and a cytotoxic compound. The glyoxalase system is involved in the detoxification of methylglyoxal. The exceptionality of the glyoxalase enzyme in the parasitic protozoa is the dependence on the dithiol – trypanothione for detoxifying the toxic methylglyoxal. The detoxification process by the glyoxalase enzyme in eukaryotes and most other organisms is dependent on the tripeptide glutathione. The glyoxalase enzyme of trypanosomatids are also exceptional in a way that they use the divalent cation nickel as a cofactor like the glyoxalase enzyme of E. coli, whereas in eukaryotes the cofactor is zinc. This reflects that both the substrate as well as the cofactor of the kinetoplastids glyoxalase enzyme is distinct from that of the glyoxalase enzyme of eukaryotes. These differences reveal that the active site of the glyoxalase enzyme of the parasite and its mammalian counterpart are significantly different thereby proposing that the glyoxalase enzyme of the protozoan parasite can be a potential chemotherapeutic target

A glutathione-specific aldose reductase of Leishmania donovani and its potential implications for methylglyoxal detoxification pathway

Methylglyoxal is mainly catabolized by two major enzymatic pathways. The first is the ubiquitous detoxification pathway, the glyoxalase pathway. In addition to the glyoxalase pathway, aldose reductase pathway also plays a crucial role in lowering the levels of methylglyoxal. The gene encoding aldose reductase (ALR) has been cloned from Leishmania donovani, a protozoan parasite causing visceral leishmaniasis. DNA sequence analysis revealed an open reading frame (ORF) of ~855 bp encoding a putative protein of 284 amino acids with a calculated molecular mass of 31.7 kDa and a predicted isoelectric point of 5.85. The sequence identity between L. donovani ALR (LdALR) and mammals and plants is only 36-44%. The ORF is a single copy gene. A protein with a molecular mass that matched the estimated ~74 kDa according to the amino acid composition of LdALR with a maltose binding tag present at its N-terminal end was induced by heterologous expression of LdALR in Escherichia coli. In the presence of glutathione, recombinant LdALR reduced methylglyoxal with a Km of ~112 µM. Comparative structural analysis of the human ALR structure with LdALR model suggests that the active site anchoring the N-terminal end of the glutathione is highly conserved. However, the C-terminal end of the glutathione backbone is expected to be exposed in LdALR, as the residues anchoring the C-terminal end of the glutathione backbone come from the three loop regions in human, which are apparently shortened in the LdALR structure. Thus, the computational analysis provides clues about the expected mode of glutathione binding and its interactions with the protein. This is the first report of the role of an ALR in the metabolic disposal of methylglyoxal in L. donovani and of thiol binding to a kinetoplastid aldose reductase

Distributed medical image analysis and diagnosis through crowd-sourced games: a malaria case study.

In this work we investigate whether the innate visual recognition and learning capabilities of untrained humans can be used in conducting reliable microscopic analysis of biomedical samples toward diagnosis. For this purpose, we designed entertaining digital games that are interfaced with artificial learning and processing back-ends to demonstrate that in the case of binary medical diagnostics decisions (e.g., infected vs. uninfected), with the use of crowd-sourced games it is possible to approach the accuracy of medical experts in making such diagnoses. Specifically, using non-expert gamers we report diagnosis of malaria infected red blood cells with an accuracy that is within 1.25% of the diagnostics decisions made by a trained medical professional