ACT-R based human digital twin to enhance operators' performance in process industries.

To ensure safe and efficient operation, operators in process industries have to make timely decisions based on time-varying information. A holistic assessment of operators' performance is, therefore, challenging. Current approaches to operator performance assessment are subjective and ignore operators' cognitive behavior. In addition, these cannot be used to predict operators' expected responses during novel situations that may arise during plant operations. The present study seeks to develop a human digital twin (HDT) that can simulate a control room operator's behavior, even during various abnormal situations. The HDT has been developed using the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture. It mimics a human operator as they monitor the process and intervene during abnormal situations. We conducted 426 trials to test the HDT's ability to handle disturbance rejection tasks. In these simulations, we varied the reward and penalty parameters to provide feedback to the HDT. We validated the HDT using the eye gaze behavior of 10 human subjects who completed 110 similar disturbance rejection tasks as that of the HDT. The results indicate that the HDT exhibits similar gaze behaviors as the human subjects, even when dealing with abnormal situations. These indicate that the HDT's cognitive capabilities are comparable to those of human operators. As possible applications, the proposed HDT can be used to generate a large database of human behavior during abnormalities which can then be used to spot and rectify flaws in novice operator's mental models. Additionally, the HDT can also enhance operators' decision-making during real-time operation.

A comparative analysis of heartworm medication use patterns for dogs that also receive ectoparasiticides.

BACKGROUND: Heartworm medications and many oral or topical flea and tick products are provided as monthly doses while a newer oral flea/tick product, fluralaner (BRAVECTO® Chew), is re-dosed at a 12-week interval. This study focused on whether there was a difference in the number of heartworm medication doses that were purchased in the 12-months follow-up period for dogs that receive either fluralaner or other flea/tick medications that are dosed monthly. METHODS: Clinic transaction records of heartworm medication purchases for over 200,000 dogs were examined to compare the purchase of heartworm preventative protection by dog owners that also receive flea and tick medications of differing efficacy durations. RESULTS: Annual purchases of heartworm medication for dogs by owners that receive a flea and tick medication dosed at 12-week intervals was incrementally higher than the number of doses purchased for dogs receiving monthly flea and tick medications. The average number of monthly doses per year was slightly over 7 months for both categories of product. The distribution of purchases of monthly doses was also similar between groups. CONCLUSIONS: Dog owners who purchase a longer-acting flea and tick medication purchase as much heartworm medication annually for their dogs as dog owners who purchase monthly flea and tick medication. On average, dog owners who gave their dog fluralaner obtained significantly more months of heartworm preventative protection compared with dog owners who gave their dog a monthly flea and tick medication, although the biological significance of this increase in doses is very small.

An efficient graph theory based method to identify every minimal reaction set in a metabolic network.

BACKGROUND: Development of cells with minimal metabolic functionality is gaining importance due to their efficiency in producing chemicals and fuels. Existing computational methods to identify minimal reaction sets in metabolic networks are computationally expensive. Further, they identify only one of the several possible minimal reaction sets. RESULTS: In this paper, we propose an efficient graph theory based recursive optimization approach to identify all minimal reaction sets. Graph theoretical insights offer systematic methods to not only reduce the number of variables in math programming and increase its computational efficiency, but also provide efficient ways to find multiple optimal solutions. The efficacy of the proposed approach is demonstrated using case studies from Escherichia coli and Saccharomyces cerevisiae. In case study 1, the proposed method identified three minimal reaction sets each containing 38 reactions in Escherichia coli central metabolic network with 77 reactions. Analysis of these three minimal reaction sets revealed that one of them is more suitable for developing minimal metabolism cell compared to other two due to practically achievable internal flux distribution. In case study 2, the proposed method identified 256 minimal reaction sets from the Saccharomyces cerevisiae genome scale metabolic network with 620 reactions. The proposed method required only 4.5 hours to identify all the 256 minimal reaction sets and has shown a significant reduction (approximately 80%) in the solution time when compared to the existing methods for finding minimal reaction set. CONCLUSIONS: Identification of all minimal reactions sets in metabolic networks is essential since different minimal reaction sets have different properties that effect the bioprocess development. The proposed method correctly identified all minimal reaction sets in a both the case studies. The proposed method is computationally efficient compared to other methods for finding minimal reaction sets and useful to employ with genome-scale metabolic networks.

Reconstruction and analysis of a genome-scale metabolic model for Scheffersomyces stipitis.

BACKGROUND: Fermentation of xylose, the major component in hemicellulose, is essential for economic conversion of lignocellulosic biomass to fuels and chemicals. The yeast Scheffersomyces stipitis (formerly known as Pichia stipitis) has the highest known native capacity for xylose fermentation and possesses several genes for lignocellulose bioconversion in its genome. Understanding the metabolism of this yeast at a global scale, by reconstructing the genome scale metabolic model, is essential for manipulating its metabolic capabilities and for successful transfer of its capabilities to other industrial microbes. RESULTS: We present a genome-scale metabolic model for Scheffersomyces stipitis, a native xylose utilizing yeast. The model was reconstructed based on genome sequence annotation, detailed experimental investigation and known yeast physiology. Macromolecular composition of Scheffersomyces stipitis biomass was estimated experimentally and its ability to grow on different carbon, nitrogen, sulphur and phosphorus sources was determined by phenotype microarrays. The compartmentalized model, developed based on an iterative procedure, accounted for 814 genes, 1371 reactions, and 971 metabolites. In silico computed growth rates were compared with high-throughput phenotyping data and the model could predict the qualitative outcomes in 74% of substrates investigated. Model simulations were used to identify the biosynthetic requirements for anaerobic growth of Scheffersomyces stipitis on glucose and the results were validated with published literature. The bottlenecks in Scheffersomyces stipitis metabolic network for xylose uptake and nucleotide cofactor recycling were identified by in silico flux variability analysis. The scope of the model in enhancing the mechanistic understanding of microbial metabolism is demonstrated by identifying a mechanism for mitochondrial respiration and oxidative phosphorylation. CONCLUSION: The genome-scale metabolic model developed for Scheffersomyces stipitis successfully predicted substrate utilization and anaerobic growth requirements. Useful insights were drawn on xylose metabolism, cofactor recycling and mechanism of mitochondrial respiration from model simulations. These insights can be applied for efficient xylose utilization and cofactor recycling in other industrial microorganisms. The developed model forms a basis for rational analysis and design of Scheffersomyces stipitis metabolic network for the production of fuels and chemicals from lignocellulosic biomass.

Decision support for green supply chain operations by integrating dynamic simulation and LCA indicators: diaper case study.

As the issue of environmental sustainability is becoming an important business factor, companies are now looking for decision support tools to assess the fuller picture of the environmental impacts associated with their manufacturing operations and supply chain (SC) activities. Lifecycle assessment (LCA) is widely used to measure the environmental consequences assignable to a product. However, it is usually limited to a high-level snapshot of the environmental implications over the product value chain without consideration of the dynamics arising from the multitiered structure and the interactions along the SC. This paper proposes a framework for green supply chain management by integrating a SC dynamic simulation and LCA indicators to evaluate both the economic and environmental impacts of various SC decisions such as inventories, distribution network configuration, and ordering policy. The advantages of this framework are demonstrated through an industrially motivated case study involving diaper production. Three distinct scenarios are evaluated to highlight how the proposed approach enables integrated decision support for green SC design and operation.

Analysis of the heat shock response in mouse liver reveals transcriptional dependence on the nuclear receptor peroxisome proliferator-activated receptor alpha (PPARalpha).

BACKGROUND: The nuclear receptor peroxisome proliferator-activated receptor alpha (PPARalpha) regulates responses to chemical or physical stress in part by altering expression of genes involved in proteome maintenance. Many of these genes are also transcriptionally regulated by heat shock (HS) through activation by HS factor-1 (HSF1). We hypothesized that there are interactions on a genetic level between PPARalpha and the HS response mediated by HSF1. RESULTS: Wild-type and PPARalpha-null mice were exposed to HS, the PPARalpha agonist WY-14,643 (WY), or both; gene and protein expression was examined in the livers of the mice 4 or 24 hrs after HS. Gene expression profiling identified a number of Hsp family members that were altered similarly in both mouse strains. However, most of the targets of HS did not overlap between strains. A subset of genes was shown by microarray and RT-PCR to be regulated by HS in a PPARalpha-dependent manner. HS also down-regulated a large set of mitochondrial genes specifically in PPARalpha-null mice that are known targets of PPARgamma co-activator-1 (PGC-1) family members. Pretreatment of PPARalpha-null mice with WY increased expression of PGC-1beta and target genes and prevented the down-regulation of the mitochondrial genes by HS. A comparison of HS genes regulated in our dataset with those identified in wild-type and HSF1-null mouse embryonic fibroblasts indicated that although many HS genes are regulated independently of both PPARalpha and HSF1, a number require both factors for HS responsiveness. CONCLUSIONS: These findings demonstrate that the PPARalpha genotype has a dramatic effect on the transcriptional targets of HS and support an expanded role for PPARalpha in the regulation of proteome maintenance genes after exposure to diverse forms of environmental stress including HS.

NIFTI: an evolutionary approach for finding number of clusters in microarray data.

BACKGROUND: Clustering techniques are routinely used in gene expression data analysis to organize the massive data. Clustering techniques arrange a large number of genes or assays into a few clusters while maximizing the intra-cluster similarity and inter-cluster separation. While clustering of genes facilitates learning the functions of un-characterized genes using their association with known genes, clustering of assays reveals the disease stages and subtypes. Many clustering algorithms require the user to specify the number of clusters a priori. A wrong specification of number of clusters generally leads to either failure to detect novel clusters (disease subtypes) or unnecessary splitting of natural clusters. RESULTS: We have developed a novel method to find the number of clusters in gene expression data. Our procedure evaluates different partitions (each with different number of clusters) from the clustering algorithm and finds the partition that best describes the data. In contrast to the existing methods that evaluate the partitions independently, our procedure considers the dynamic rearrangement of cluster members when a new cluster is added. Partition quality is measured based on a new index called Net InFormation Transfer Index (NIFTI) that measures the information change when an additional cluster is introduced. Information content of a partition increases when clusters do not intersect and decreases if they are not clearly separated. A partition with the highest Total Information Content (TIC) is selected as the optimal one. We illustrate our method using four publicly available microarray datasets. CONCLUSION: In all four case studies, the proposed method correctly identified the number of clusters and performs better than other well known methods. Our method also showed invariance to the clustering techniques.

Quantitative identification of teratoma tissues formed by human embryonic stem cells with TeratomEye.

An automated vision system, TeratomEye, was developed for the identification of three representative tissue types: muscle, gut and neural epithelia which are commonly found in teratomas formed from human embryonic stem cells. Muscle tissue, a common structure was identified with an accuracy of 90.3% with high specificity and sensitivity greater than 90%. Gut epithelia were identified with an accuracy of 87.5% with specificity and sensitivity greater than 80%. Neural epithelia which were the most difficult structures to distinguish gave an accuracy of 47.6%. TeratomEye is therefore useful for the automated identification of differentiated tissues in teratoma sections.

Unbiased stratification of left ventricles.

Image based quantitative stratification of the Left Ventricles (LV) across a population helps in unraveling the structure-function symbiosis of the heart. An unbiased, reference less grouping scheme that automatically determines the number of clusters and a physioanatomically relevant strategy that aligns the intra cluster LV shapes would enable the robust construction of pathology stratified cardiac atlas. This paper achieves this hitherto elusive stratification and alignment by adapting the conventional strategies routinely followed by clinicians. The individual LV shape models (N=127) are independently oriented to an "attitudinally consistent orientation" that captures the physioanatomic variations of the LV morphology. Affinity propagation technique based on the automatically identified inter-LV_landmark distances is used to group the LV shapes. The proposed algorithm is computationally efficient and, if the inter cluster variations are linked to pathology, could provide a clinically relevant cardiac atlas.

Principal components analysis based methodology to identify differentially expressed genes in time-course microarray data.

BACKGROUND: Time-course microarray experiments are being increasingly used to characterize dynamic biological processes. In these experiments, the goal is to identify genes differentially expressed in time-course data, measured between different biological conditions. These differentially expressed genes can reveal the changes in biological process due to the change in condition which is essential to understand differences in dynamics. RESULTS: In this paper, we propose a novel method for finding differentially expressed genes in time-course data and across biological conditions (say C1 and C2). We model the expression at C1 using Principal Component Analysis and represent the expression profile of each gene as a linear combination of the dominant Principal Components (PCs). Then the expression data from C2 is projected on the developed PCA model and scores are extracted. The difference between the scores is evaluated using a hypothesis test to quantify the significance of differential expression. We evaluate the proposed method to understand differences in two case studies (1) the heat shock response of wild-type and HSF1 knockout mice, and (2) cell-cycle between wild-type and Fkh1/Fkh2 knockout Yeast strains. CONCLUSION: In both cases, the proposed method identified biologically significant genes.

Detection of phase shifts in batch fermentation via statistical analysis of the online measurements: a case study with rifamycin B fermentation.

Industrial production of antibiotics, biopharmaceuticals and enzymes is typically carried out via a batch or fed-batch fermentation process. These processes go through various phases based on sequential substrate uptake, growth and product formation, which require monitoring due to the potential batch-to-batch variability. The phase shifts can be identified directly by measuring the concentrations of substrates and products or by morphological examinations under microscope. However, such measurements are cumbersome to obtain. We present a method to identify phase transitions in batch fermentation using readily available online measurements. Our approach is based on Dynamic Principal Component Analysis (DPCA), a multivariate statistical approach that can model the dynamics of non-stationary processes. Phase-transitions in fermentation produce distinct patterns in the DPCA scores, which can be identified as singular points. We illustrate the application of the method to detect transitions such as the onset of exponential growth phase, substrate exhaustion and substrate switching for rifamycin B fermentation batches. Further, we analyze the loading vectors of DPCA model to illustrate the mechanism by which the statistical model accounts for process dynamics. The approach can be readily applied to other industrially important processes and may have implications in online monitoring of fermentation batches in a production facility.

A simulation course on lifesaving techniques for third-year medical students.

BACKGROUND: The University of Virginia School of Medicine discontinued animal vivisection in February 2004 for teaching lifesaving procedures to third-year medical students. Consequently, a 1-day course using simulation technology was developed to meet objectives previously covered in the animal laboratory. The authors sought to evaluate the course and hypothesized that the students' confidence in lifesaving procedures as well as their acceptance of simulation technology as a teaching tool would increase. METHODS: The course was designed in a two-session format. The first session (first half of the day) concentrated on individual procedure skills, utilizing part-task trainers. The second session (second half of the day) used a Medical Education Technologies Inc. (METI) Emergency Care Simulator (ECS) full-body patient simulator to present a major trauma scenario. The study design was a prospective, pretest-posttest study without a control group. A 10-question pre and post survey used a Likert scale to explore students' confidence in their skills as well as their acceptance of simulation technology. A course evaluation used a similar Likert scale for evaluation of the course substations, the trauma scenario, and students' self-assessment of their skill levels as well as a 100% point scale for an overall rating of the course. RESULTS: A total of eight 1-day courses were successfully held over 2 years with a total enrollment of 240 students utilizing 20 instructors inclusive of faculty, residents, and other emergency medicine health care providers. For the pre and post survey results, there was a significant increase in students' confidence in performing lifesaving procedures as well as their acceptance of simulation as a teaching tool (P < 0.05 for each question with pre n = 222 and post n = 226). For the course evaluation results (n = 190), all of the course substations were rated in the good to excellent range and the course received an overall score of 97.55 +/- 7.23% out of 100%. Furthermore, students reported a significant increase in their skill level (P < 0.05). CONCLUSION: This lifesaving techniques course utilizing simulation technology successfully covered objectives previously taught with animal vivisection, increased students' confidence levels in performing lifesaving procedures and was highly accepted by the medical students.

The interrater variation of ED abdominal examination findings in patients with acute abdominal pain.

OBJECTIVE: The physical examination of the abdomen is crucial to emergency department (ED) management of patients with abdominal pain. We sought to determine the interrater variation between attending and resident physicians in detecting abdominal exam findings. METHODS: Research enrollers surveyed attending and resident physicians on abdominal exam findings in the ED in patients with abdominal pain. Strength of agreement was calculated using the kappa statistic. RESULTS: A convenience sample of 122 surveys was completed. Calculated kappa results are in parentheses. There was almost perfect agreement on the presence of masses and substantial agreement on the need for imaging studies. There was moderate agreement on guarding, distension, tenderness, and need for laboratory tests and surgical consultation. For 88 (72%) patients with tenderness, substantial agreement was calculated for epigastric tenderness, moderate agreement on right upper quadrant, supraumbilical, suprapubic, left lower quadrant, right lower quadrant tenderness, and fair agreement on left upper quadrant tenderness. Sixty-one (50%) patients received pain medicine in the ED. Among those, there was fair agreement on a presence of a surgical abdomen. Upper level resident physicians noted a higher level of agreement with the attending physician for tenderness than junior resident physicians. CONCLUSIONS: There was moderate agreement between resident and attending physicians for most of the findings in patients with abdominal pain. Recognition that selected findings are more variable than others should encourage careful confirmation of resident physicians' assessments in teaching settings.

Society for Academic Emergency Medicine survey of the Association of American Medical Colleges Council of Academic Societies.

The Society for Academic Emergency Medicine (SAEM) surveyed the Council of Academic Societies (CAS) organizations to obtain useful information to project SAEM goals into the year 2010. The objective of this work was to understand common and varying organizational operations and identify opportunities. The authors reviewed CAS organizations' mission statements, operating budgets, modes of communications, products, meeting formats, foundations, endowment funds, staff structures, headquarters, advocacy activities (services), top challenges, and most significant changes anticipated the next ten years. The survey methodology was used to gain insight into modes of operation of CAS organizations and enable SAEM to review its own operations and identify potential organizational changes based on the experiences of others. Individual CAS organizations might similarly benefit by reviewing the results of the survey and comparing themselves with others.

Integrated decision support system for waste minimization analysis in chemical processes.

The need to build and operate environmentally friendly plants has challenged the chemical industry to consider waste minimization or even elimination starting from the early stages of process development. A thorough waste minimization analysis requires specialized expertise and is laborious, time-consuming, expensive, and knowledge-intensive. This has caused a major technical barrier for implementing waste minimization programswithin the industry. Previously, we had reported a systematic methodology and a knowledge-based system, called ENVOPExpert, for identifying waste minimization opportunities in chemical processes. In this paper, we propose an integrated qualitative-quantitative methodology to identify waste minimization alternatives and assess their efficacy in terms of environmental impact and process economics. A qualitative analysis is first conducted to identify the sources of wastes and to propose alternatives for eliminating or minimizing them. Environmental impact of each alternative is then calculated by doing a quantitative pollutant balance. The capital expenditure required for implementing the alternative and the resulting plant operating costs are also calculated and used in the evaluation of the waste minimization alternatives. Through this, practical and cost-effective options can be identified. This methodology has been implemented as an integrated decision support system and tested using the hydrodealkylation process case study with satisfactory results.