ASU Scholarship Showcase

The estimation of energy demand (by power plants) has traditionally relied on historical energy use data for the region(s) that a plant produces for. Regression analysis, artificial neural network and Bayesian theory are the most common approaches for analysing these data. Such data and techniques do not generate reliable results. Consequently, excess energy has to be generated to prevent blackout; causes for energy surge are not easily determined; and potential energy use reduction from energy efficiency solutions is usually not translated into actual energy use reduction. The paper highlights the weaknesses of traditional techniques, and lays out a framework to improve the prediction of energy demand by combining energy use models of equipment, physical systems and buildings, with the proposed data mining algorithms for reverse engineering. The research team first analyses data samples from large complex energy data, and then, presents a set of computationally efficient data mining algorithms for reverse engineering. In order to develop a structural system model for reverse engineering, two focus groups are developed that has direct relation with cause and effect variables. The research findings of this paper includes testing out different sets of reverse engineering algorithms, understand their output patterns and modify algorithms to elevate accuracy of the outputs.

Small and medium office buildings consume a significant parcel of the U.S. building stock energy consumption. Still, owners lack resources and experience to conduct detailed energy audits and retrofit analysis. We present an eight-steps framework for an energy retrofit assessment in small and medium office buildings. Through a bottom-up approach and a web-based retrofit toolkit tested on a case study in Arizona, this methodology was able to save about 50% of the total energy consumed by the case study building, depending on the adopted measures and invested capital. While the case study presented is a deep energy retrofit, the proposed framework is effective in guiding the decision-making process that precedes any energy retrofit, deep or light.

Commercial buildings’ consumption is driven by multiple factors that include occupancy, system and equipment efficiency, thermal heat transfer, equipment plug loads, maintenance and operational procedures, and outdoor and indoor temperatures. A modern building energy system can be viewed as a complex dynamical system that is interconnected and influenced by external and internal factors. Modern large scale sensor measures some physical signals to monitor real-time system behaviors. Such data has the potentials to detect anomalies, identify consumption patterns, and analyze peak loads. The paper proposes a novel method to detect hidden anomalies in commercial building energy consumption system. The framework is based on Hilbert-Huang transform and instantaneous frequency analysis. The objectives are to develop an automated data pre-processing system that can detect anomalies and provide solutions with real-time consumption database using Ensemble Empirical Mode Decomposition (EEMD) method. The finding of this paper will also include the comparisons of Empirical mode decomposition and Ensemble empirical mode decomposition of three important type of institutional buildings.

There are many data mining and machine learning techniques to manage large sets of complex energy supply and demand data for building, organization and city. As the amount of data continues to grow, new data analysis methods are needed to address the increasing complexity. Using data from the energy loss between the supply (energy production sources) and demand (buildings and cities consumption), this paper proposes a Semi-Supervised Energy Model (SSEM) to analyse different loss factors for a building cluster. This is done by deep machine learning by training machines to semi-supervise the learning, understanding and manage the process of energy losses. Semi-Supervised Energy Model (SSEM) aims at understanding the demand-supply characteristics of a building cluster and utilizes the confident unlabelled data (loss factors) using deep machine learning techniques. The research findings involves sample data from one of the university campuses and presents the output, which provides an estimate of losses that can be reduced. The paper also provides a list of loss factors that contributes to the total losses and suggests a threshold value for each loss factor, which is determined through real time experiments. The conclusion of this paper provides a proposed energy model that can provide accurate numbers on energy demand, which in turn helps the suppliers to adopt such a model to optimize their supply strategies.

Introduction: Abundance of immune cells has been shown to have prognostic and predictive significance in many tumor types. Beyond abundance, the spatial organization of immune cells in relation to cancer cells may also have significant functional and clinical implications. However there is a lack of systematic methods to quantify spatial associations between immune and cancer cells.

Methods: We applied ecological measures of species interactions to digital pathology images for investigating the spatial associations of immune and cancer cells in breast cancer. We used the Morisita-Horn similarity index, an ecological measure of community structure and predator–prey interactions, to quantify the extent to which cancer cells and immune cells colocalize in whole-tumor histology sections. We related this index to disease-specific survival of 486 women with breast cancer and validated our findings in a set of 516 patients from different hospitals.

Results: Colocalization of immune cells with cancer cells was significantly associated with a disease-specific survival benefit for all breast cancers combined. In HER2-positive subtypes, the prognostic value of immune-cancer cell colocalization was highly significant and exceeded those of known clinical variables. Furthermore, colocalization was a significant predictive factor for long-term outcome following chemotherapy and radiotherapy in HER2 and Luminal A subtypes, independent of and stronger than all known clinical variables.

Conclusions: Our study demonstrates how ecological methods applied to the tumor microenvironment using routine histology can provide reproducible, quantitative biomarkers for identifying high-risk breast cancer patients. We found that the clinical value of immune-cancer interaction patterns is highly subtype-specific but substantial and independent to known clinicopathologic variables that mostly focused on cancer itself. Our approach can be developed into computer-assisted prediction based on histology samples that are already routinely collected.

In a meta-analysis published by myself and co-authors, we report differences in the life history risk factors for estrogen receptor negative (ER−) and estrogen receptor positive (ER+) breast cancers. Our meta-analysis did not find the association of ER− breast cancer risk with fast life history characteristics that Hidaka and Boddy suggest in their response to our article. There are a number of possible explanations for the differences between their conclusions and the conclusions we drew from our meta-analysis, including limitations of our meta-analysis and methodological challenges in measuring and categorizing estrogen receptor status. These challenges, along with the association of ER+ breast cancer with slow life history characteristics, may make it challenging to find a clear signal of ER− breast cancer with fast life history characteristics, even if that relationship does exist. The contradictory results regarding breast cancer risk and life history characteristics illustrate a more general challenge in evolutionary medicine: often different sub-theories in evolutionary biology make contradictory predictions about disease risk. In this case, life history models predict that breast cancer risk should increase with faster life history characteristics, while the evolutionary mismatch hypothesis predicts that breast cancer risk should increase with delayed reproduction. Whether life history tradeoffs contribute to ER− breast cancer is still an open question, but current models and several lines of evidence suggest that it is a possibility.

It has long been accepted that modern reproductive patterns are likely contributors to breast cancer susceptibility because of their influence on hormones such as estrogen and the importance of these hormones in breast cancer. We conducted a meta-analysis to assess whether this ‘evolutionary mismatch hypothesis’ can explain susceptibility to both estrogen receptor positive (ER-positive) and estrogen receptor negative (ER-negative) cancer. Our meta-analysis includes a total of 33 studies and examines parity, age of first birth and age of menarche broken down by estrogen receptor status. We found that modern reproductive patterns are more closely linked to ER-positive than ER-negative breast cancer. Thus, the evolutionary mismatch hypothesis for breast cancer can account for ER-positive breast cancer susceptibility but not ER-negative breast cancer.

The low pH of the stomach serves as a barrier to ingested microbes and must be overcome or bypassed when delivering live bacteria for vaccine or probiotic applications. Typically, the impact of stomach acidity on bacterial survival is evaluated in vitro, as there are no small animal models to evaluate these effects in vivo. To better understand the effect of this low pH barrier to live attenuated Salmonella vaccines, which are often very sensitive to low pH, we investigated the value of the histamine mouse model for this application. A low pH gastric compartment was transiently induced in mice by the injection of histamine. This resulted in a gastric compartment of approximately pH 1.5 that was capable of distinguishing between acid-sensitive and acid-resistant microbes. Survival of enteric microbes during gastric transit in this model directly correlated with their in vitro acid resistance. Because many Salmonella enterica serotype Typhi vaccine strains are sensitive to acid, we have been investigating systems to enhance the acid resistance of these bacteria. Using the histamine mouse model, we demonstrate that the in vivo survival of S. Typhi vaccine strains increased approximately 10-fold when they carried a sugar-inducible arginine decarboxylase system. We conclude that this model will be a useful for evaluating live bacterial preparations prior to clinical trials.

Leucine-responsive regulatory protein (Lrp) is known to be an indirect activator of type 1 fimbriae synthesis in Salmonella enterica serovar Typhimurium via direct regulation of FimZ, a direct positive regulator for type 1 fimbriae production. Using RT-PCR, we have shown previously that fimA transcription is dramatically impaired in both lrp-deletion (Δlrp) and constitutive-lrp expression (lrp^C) mutant strains. In this work, we used chromosomal P_fimA-lacZ fusions and yeast agglutination assays to confirm and extend our previous results. Direct binding of Lrp to P_fimA was shown by an electrophoretic mobility shift assay (EMSA) and DNA footprinting assay. Site-directed mutagenesis revealed that the Lrp-binding motifs in P_fimA play a role in both activation and repression of type 1 fimbriae production. Overproduction of Lrp also abrogates fimZ expression. EMSA data showed that Lrp and FimZ proteins independently bind to P_fimA without competitive exclusion. In addition, both Lrp and FimZ binding to P_fimA caused a hyper retardation (supershift) of the DNA-protein complex compared to the shift when each protein was present alone. Nutrition-dependent cellular Lrp levels closely correlated with the amount of type 1 fimbriae production. These observations suggest that Lrp plays important roles in type 1 fimbriation by acting as both a positive and negative regulator and its effect depends, at least in part, on the cellular concentration of Lrp in response to the nutritional environment.

Researchers have iterated that the future of synthetic biology and biotechnology lies in novel consumer applications of crossing biology with engineering. However, if the new biology's future is to be sustainable, early and serious efforts must be made towards social sustainability. Therefore, the crux of new applications of synthetic biology and biotechnology is public understanding and acceptance. The RASVaccine is a novel recombinant design not found in nature that re-engineers a common bacteria ( Salmonella) to produce a strong immune response in humans. Synthesis of the RASVaccine has the potential to improve public health as an inexpensive, non-injectable product. But how can scientists move forward to create a dialogue of creating a 'common sense' of this new technology in order to promote social sustainability? This paper delves into public issues raised around these novel technologies and uses the RASVaccine as an example of meeting the public with a common sense of its possibilities and limitations.