Neural Shrubs: Using Neural Networks to Improve Decision Trees

Decision trees are a method commonly used in machine learning to either predict a categorical response or a continuous response variable. Once the tree partitions the space, the response is either determined by the majority vote – classification trees, or by averaging the response values – regression trees. This research builds a standard regression tree and then instead of averaging the responses, we train a neural network to determine the response value. We have found that our approach typically increases the predicative capability of the decision tree. We have 2 demonstrations of this approach that we wish to present as a poster at the SDSU Data Symposium

An inkjet printed, roll-coated digital microfluidic device for inexpensive, miniaturized diagnostic assays

The diagnosis of infectious disease is typically carried out at the point-of-care (POC) using the lateral flow assay (LFA). While cost-effective and portable, LFAs often lack the clinical sensitivity and specificity required for accurate diagnoses. In response to this challenge, we introduce a new digital microfluidic (DMF) platform fabricated using a custom inkjet printing and roll-coating process that is scalable to mass production. The performance of the new devices is on par with that of traditional DMF devices fabricated in a cleanroom, with a materials cost for the new devices of only US $0.63 per device. To evaluate the usefulness of the new platform, we performed a 13-step rubella virus (RV) IgG immunoassay on the inkjet printed, roll-coated devices, which yielded a limit of detection of 0.02 IU mL^(−1), well below the diagnostic cut-off of 10 IU mL^(−1) for RV infection and immunity. We propose that this represents a breakthrough for DMF, lowering the costs to a level such that the new platforms will be an attractive alternative to LFAs for the diagnosis of infectious disease at the POC

Electrochemiluminescence on digital microfluidics for microRNA analysis

Electrochemiluminescence (ECL) is a sensitive analytical technique with great promise for biological applications, especially when combined with microfluidics. Here, we report the first integration of ECL with digital microfluidics (DMF). ECL detectors were fabricated into the ITO-coated top plates of DMF devices, allowing for the generation of light from electrically excited luminophores in sample droplets. The new system was characterized by making electrochemical and ECL measurements of soluble mixtures of tris(phenanthroline)ruthenium(II) and tripropylamine (TPA) solutions. The system was then validated by application to an oligonucleotide hybridization assay, using magnetic particles bearing 21-mer, deoxyribose analogues of the complement to microRNA-143 (miRNA-143). The system detects single nucleotide mismatches with high specificity, and has a limit of detection of 1.5 femtomoles. The system is capable of detecting miRNA-143 in cancer cell lysates, allowing for the discrimination between the MCF-7 (less aggressive) and MDA-MB-231 (more aggressive) cell lines. We propose that DMF-ECL represents a valuable new tool in the microfluidics toolbox for a wide variety of applications

An inkjet printed, roll-coated digital microfluidic device for inexpensive, miniaturized diagnostic assays

The diagnosis of infectious disease is typically carried out at the point-of-care (POC) using the lateral flow assay (LFA). While cost-effective and portable, LFAs often lack the clinical sensitivity and specificity required for accurate diagnoses. In response to this challenge, we introduce a new digital microfluidic (DMF) platform fabricated using a custom inkjet printing and roll-coating process that is scalable to mass production. The performance of the new devices is on par with that of traditional DMF devices fabricated in a cleanroom, with a materials cost for the new devices of only US $0.63 per device. To evaluate the usefulness of the new platform, we performed a 13-step rubella virus (RV) IgG immunoassay on the inkjet printed, roll-coated devices, which yielded a limit of detection of 0.02 IU mL^(−1), well below the diagnostic cut-off of 10 IU mL^(−1) for RV infection and immunity. We propose that this represents a breakthrough for DMF, lowering the costs to a level such that the new platforms will be an attractive alternative to LFAs for the diagnosis of infectious disease at the POC

A digital microfluidic system for serological immunoassays in remote settings

Serosurveys are useful for assessing population susceptibility to vaccine-preventable disease outbreaks. Although at-risk populations in remote areas could benefit from this type of information, they face several logistical barriers to implementation, such as lack of access to centralized laboratories, cold storage, and transport of samples. We describe a potential solution: a compact and portable, field-deployable, point-of-care system relying on digital microfluidics that can rapidly test a small volume of capillary blood for disease-specific antibodies. This system uses inexpensive, inkjet-printed digital microfluidic cartridges together with an integrated instrument to perform enzyme-linked immunosorbent assays (ELISAs). We performed a field validation of the system’s analytical performance at Kakuma refugee camp, a remote setting in northwestern Kenya, where we tested children aged 9 to 59 months and caregivers for measles and rubella immunoglobulin G (IgG). The IgG assays were determined to have sensitivities of 86% [95% confidence interval (CI), 79 to 91% (measles)] and 81% [95% CI, 73 to 88% (rubella)] and specificities of 80% [95% CI, 49 to 94% (measles)] and 91% [95% CI, 76 to 97% (rubella)] (measles, n = 140; rubella, n = 135) compared with reference tests (measles IgG and rubella IgG ELISAs from Siemens Enzygnost) conducted in a centralized laboratory. These results demonstrate a potential role for this point-of-care system in global serological surveillance, particularly in remote areas with limited access to centralized laboratories

Behavior Detectors to Support Feedback Generation using Problem-Solving Action Data

Feedback is an essential component of learning and a key difficulty in achieving quality education at scale. Providing feedback is often a tedious task and there is a paucity of resources to aid teachers. In this work, we expand on previous tools that focus on generating natural language feedback for open response questions. Computer-based systems have the unique advantage of being able to collect action-by-action reports of the steps a student took to reach an answer along with metadata, such as time spent on a problem. It is difficult for teachers to analyze the detailed metadata when providing feedback, but it presents us with an opportunity to distill information from it. We take on problem-solving action data to provide teachers with detectors of student behavior. These detectors can be used to better keep track of their students' activity and inform what feedback can be provided

The effects of bi-functional anti-adhesion scaffolds on flexor tendon healing in a rabbit model

10.1002/jbm.b.3407710672605-261

Digital Microfluidic Platform for Human Plasma Protein Depletion

Many important biomarkers for disease diagnosis are present at low concentrations in human serum. These biomarkers are masked in proteomic analysis by highly abundant proteins such as human serum albumin (HSA) and immunoglobulins (IgGs) which account for up to 80% of the total protein content of serum. Traditional depletion methods using macro-scale LC-columns for highly abundant proteins involve slow separations which impart considerable dilution to the samples. Furthermore, most techniques lack the ability to process multiple samples simultaneously. We present a method of protein depletion using superparamagnetic beads coated in anti-HSA, Protein A, and Protein G, manipulated by digital microfluidics (DMF). The depletion process was capable of up to 95% protein depletion efficiency for IgG and HSA in 10 min for four samples simultaneously, which resulted in an approximately 4-fold increase in signal-to-noise ratio in MALDI-MS analysis for a low abundance protein, hemopexin. This rapid and automated method has the potential to greatly improve the process of biomarker identification

Digital Microfluidic Platform for Human Plasma Protein Depletion

Many important biomarkers for disease diagnosis are present at low concentrations in human serum. These biomarkers are masked in proteomic analysis by highly abundant proteins such as human serum albumin (HSA) and immunoglobulins (IgGs) which account for up to 80% of the total protein content of serum. Traditional depletion methods using macro-scale LC-columns for highly abundant proteins involve slow separations which impart considerable dilution to the samples. Furthermore, most techniques lack the ability to process multiple samples simultaneously. We present a method of protein depletion using superparamagnetic beads coated in anti-HSA, Protein A, and Protein G, manipulated by digital microfluidics (DMF). The depletion process was capable of up to 95% protein depletion efficiency for IgG and HSA in 10 min for four samples simultaneously, which resulted in an approximately 4-fold increase in signal-to-noise ratio in MALDI-MS analysis for a low abundance protein, hemopexin. This rapid and automated method has the potential to greatly improve the process of biomarker identification