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Open Access Publications from the University of California

This series is home to publications and data sets from the Bourns College of Engineering at the University of California, Riverside.

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Center for Environmental Research and Technology

Cover page of Single and binary protein electroultrafiltration using poly(vinyl-alcohol)-carbon nanotube (PVA-CNT) composite membranes

Single and binary protein electroultrafiltration using poly(vinyl-alcohol)-carbon nanotube (PVA-CNT) composite membranes

(2020)

AbstractElectrically conductive composite ultrafiltration membranes composed of carbon nanotubes have exhibited efficient fouling inhibition in wastewater treatment applications. In the current study, poly(vinyl-alcohol)-carbon nanotube membranes were applied to fed batch crossflow electroultrafiltration of dilute (0.1 g/L of each species) single and binary protein solutions of α-lactalbumin and hen egg-white lysozyme at pH 7.4, 4 mM ionic strength, and 1 psi. Electroultrafiltration using the poly(vinyl-alcohol)-carbon nanotube composite membranes yielded temporary enhancements in sieving for single protein filtration and in selectivity for binary protein separation compared to ultrafiltration using the unmodified PS-35 membranes. Assessment of membrane fouling based on permeate flux, zeta potential measurements, and scanning electron microscopy visualization of the conditioned membranes indicated significant resulting protein adsorption and aggregation which limited the duration of improvement during electroultrafiltration with an applied cathodic potential of −4.6 V (vs. Ag/AgCl). These results imply that appropriate optimization of electroultrafiltration using carbon nanotube-deposited polymeric membranes may provide substantial short-term improvements in binary protein separations.

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Cover page of The latency of peroxisomal catalase in terms of effectiveness factor for pancreatic and glioblastoma cancer cell lines in the presence of high concentrations of H2O2: Implications for the use of pharmacological ascorbate in cancer therapy.

The latency of peroxisomal catalase in terms of effectiveness factor for pancreatic and glioblastoma cancer cell lines in the presence of high concentrations of H2O2: Implications for the use of pharmacological ascorbate in cancer therapy.

(2020)

Previous research has identified variation in cancer cell line response to high levels of extracellular H2O2 (eH2O2) exposure. This directly contributes to our understanding cellular efficacy of pharmacological ascorbate (P-AscH-) therapy. Here we investigate the factors contributing to latency of peroxisomal catalase of a cell and the importance of latency in evaluating cell exposure to eH2O2. First, we develop a mathematical framework for the latency of catalase in terms of an effectiveness factor, ηeff, to describe the catalase activity in the presence of high levels of eH2O2. A simplified relationship emerges, [Formula: see text] when mprp/Dij≪1, where mp,rp, and [Formula: see text] are the experimentally determined peroxisome permeability, average peroxisome radius, and the pseudo first-order reaction rate constant, respectively. [Formula: see text] is the catalase concentration in the peroxisome and k2=1.7x107M-1s-1. Next, previously published parameters are used to determine the latency effect of the cell lines: normal pancreatic cells (H6c7), pancreatic cancer cells (MIA PaCa-2), and glioblastoma cells (LN-229, T98G, and U-87), all which vary in their susceptibility to exposure to high eH2O2. The results show that effectiveness is not significantly different except for the most susceptible, MIA PaCa-2 cell line, which is higher when compared to all other cell lines. This result is counterintuitive and further implies that latency, as a single parameter, is ineffective in forecasting cell line susceptibility to P-AscH- therapy equivalent eH2O. Thus, further research remains necessary to identify why cancer cells vary in susceptibility to P-AscH- therapy.

Cover page of Hamming Approximation of NP Witnesses

Hamming Approximation of NP Witnesses

(2020)

Given a satisfiable 3-SAT formula, how hard is it to find an assignment to the variables that has Hamming distance at most n/2 to a satisfying assignment? More generally, consider any polynomial-time verifier for any NP-complete language. A d(n)-Hamming-approximation algorithm for the verifier is one that, given any member x of the language, outputs in polynomial time a string a with Hamming distance at most d(n) to some witness w, where (x,w) is accepted by the verifier. Previous results have shown that, if P != NP, then every NP-complete language has a verifier for which there is no (n/2-n^(2/3+d))-Hamming-approximation algorithm, for various constants d > 0. Our main result is that, if P != NP, then every paddable NP-complete language has a verifier that admits no (n/2+O(sqrt(n log n)))-Hamming-approximation algorithm. That is, one cannot get even half the bits right. We also consider natural verifiers for various well-known NP-complete problems. They do have n/2-Hamming-approximation algorithms, but, if P != NP, have no (n/2-n^epsilon)-Hamming-approximation algorithms for any constant epsilon > 0. We show similar results for randomized algorithms.

On Huang and Wong's Algorithm for Generalized Binary Split Trees

(2020)

Huang and Wong [5] proposed a polynomial-time dynamic-programming algorithm for computing optimal generalized binary split trees. We show that their algorithm is incorrect. Thus, it remains open whether such trees can be computed in polynomial time. Spuler [11, 12] proposed modifying Huang and Wong's algorithm to obtain an algorithm for a different problem: computing optimal two-way-comparison search trees. We show that the dynamic program underlying Spuler's algorithm is not valid, in that it does not satisfy the necessary optimal-substructure property and its proposed recurrence relation is incorrect. It remains unknown whether the algorithm is guaranteed to compute a correct overall solution.

Nearly Linear-Work Algorithms for Mixed Packing/Covering and Facility-Location Linear Programs

(2020)

We describe the first nearly linear-time approximation algorithms for explicitly given mixed packing/covering linear programs, and for (non-metric) fractional facility location. We also describe the first parallel algorithms requiring only near-linear total work and finishing in polylog time. The algorithms compute $(1+\epsilon)$-approximate solutions in time (and work) $O^*(N/\epsilon^2)$, where $N$ is the number of non-zeros in the constraint matrix. For facility location, $N$ is the number of eligible client/facility pairs.

A Bound on the Sum of Weighted Pairwise Distances of Points Constrained to Balls

(2020)

We consider the problem of choosing Euclidean points to maximize the sum of their weighted pairwise distances, when each point is constrained to a ball centered at the origin. We derive a dual minimization problem and show strong duality holds (i.e., the resulting upper bound is tight) when some locally optimal configuration of points is affinely independent. We sketch a polynomial time algorithm for finding a near-optimal set of points.

Lecture Notes on Evasiveness of Graph Properties

(2020)

This report presents notes from the first eight lectures of the class Many Models of Complexity taught by Laszlo Lovasz at Princeton University in the fall of 1990. The topic is evasiveness of graph properties: given a graph property, how many edges of the graph an algorithm must check in the worst case before it knows whether the property holds.

Approximating 1-dimensional TSP Requires Omega(n log n) Comparisons

(2020)

We give a short proof that any comparison-based n^(1-epsilon)-approximation algorithm for the 1-dimensional Traveling Salesman Problem (TSP) requires Omega(n log n) comparisons.

Cover page of Somatic SF3B1 hotspot mutation in prolactinomas.

Somatic SF3B1 hotspot mutation in prolactinomas.

(2020)

The genetic basis and corresponding clinical relevance of prolactinomas remain poorly understood. Here, we perform whole genome sequencing (WGS) on 21 patients with prolactinomas to detect somatic mutations and then validate the mutations with digital polymerase chain reaction (PCR) analysis of tissue samples from 227 prolactinomas. We identify the same hotspot somatic mutation in splicing factor 3 subunit B1 (SF3B1R625H) in 19.8% of prolactinomas. These patients with mutant prolactinomas display higher prolactin (PRL) levels (p = 0.02) and shorter progression-free survival (PFS) (p = 0.02) compared to patients without the mutation. Moreover, we identify that the SF3B1R625H mutation causes aberrant splicing of estrogen related receptor gamma (ESRRG), which results in stronger binding of pituitary-specific positive transcription factor 1 (Pit-1), leading to excessive PRL secretion. Thus our study validates an important mutation and elucidates a potential mechanism underlying the pathogenesis of prolactinomas that may lead to the development of targeted therapeutics.