Ras controls melanocyte expansion during zebrafish fin stripe regeneration.

Regenerative medicine for complex tissues like limbs will require the provision or activation of precursors for different cell types, in the correct number, and with the appropriate instructions. These strategies can be guided by what is learned from spectacular events of natural limb or fin regeneration in urodele amphibians and teleost fish. Following zebrafish fin amputation, melanocyte stripes faithfully regenerate in tandem with complex fin structures. Distinct populations of melanocyte precursors emerge and differentiate to pigment regenerating fins, yet the regulation of their proliferation and patterning is incompletely understood. Here, we found that transgenic increases in active Ras dose-dependently hyperpigmented regenerating zebrafish fins. Lineage tracing and marker analysis indicated that increases in active Ras stimulated the in situ amplification of undifferentiated melanocyte precursors expressing mitfa and kita. Active Ras also hyperpigmented early fin regenerates of kita mutants, which are normally devoid of primary regeneration melanocytes, suppressing defects in precursor function and survival. By contrast, this protocol had no noticeable impact on pigmentation by secondary regulatory melanocyte precursors in late-stage kita regenerates. Our results provide evidence that Ras activity levels control the repopulation and expansion of adult melanocyte precursors after tissue loss, enabling the recovery of patterned melanocyte stripes during zebrafish appendage regeneration.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Implant healing in experimental animal models of diabetes.

Diabetes mellitus is becoming increasingly prevalent worldwide. Additionally, there is an increasing number of patients receiving implantable devices such as glucose sensors and orthopedic implants. Thus, it is likely that the number of diabetic patients receiving these devices will also increase. Even though implantable medical devices are considered biocompatible by the Food and Drug Administration, the adverse tissue healing that occurs adjacent to these foreign objects is a leading cause of their failure. This foreign body response leads to fibrosis, encapsulation of the device, and a reduction or cessation of device performance. A second adverse event is microbial infection of implanted devices, which can lead to persistent local and systemic infections and also exacerbates the fibrotic response. Nearly half of all nosocomial infections are associated with the presence of an indwelling medical device. Events associated with both the foreign body response and implant infection can necessitate device removal and may lead to amputation, which is associated with significant morbidity and cost. Diabetes mellitus is generally indicated as a risk factor for the infection of a variety of implants such as prosthetic joints, pacemakers, implantable cardioverter defibrillators, penile implants, and urinary catheters. Implant infection rates in diabetic patients vary depending upon the implant and the microorganism, however, for example, diabetes was found to be a significant variable associated with a nearly 7.2% infection rate for implantable cardioverter defibrillators by the microorganism Candida albicans. While research has elucidated many of the altered mechanisms of diabetic cutaneous wound healing, the internal healing adjacent to indwelling medical devices in a diabetic model has rarely been studied. Understanding this healing process is crucial to facilitating improved device design. The purpose of this article is to summarize the physiologic factors that influence wound healing and infection in diabetic patients, to review research concerning diabetes and biomedical implants and device infection, and to critically analyze which diabetic animal model might be advantageous for assessing internal healing adjacent to implanted devices.

Bioburden after Staphylococcus aureus inoculation in type 1 diabetic rats undergoing internal fixation.

SUMMARY: Fracture stabilization in the diabetic patient is associated with higher complication rates, particularly infection and impaired wound healing, which can lead to major tissue damage, osteomyelitis, and higher amputation rates. With an increasing prevalence of diabetes and an aging population, the risks of infection of internal fixation devices are expected to grow. Although numerous retrospective clinical studies have identified a relationship between diabetes and infection, currently there are few animal models that have been used to investigate postoperative surgical-site infections associated with internal fixator implantation and diabetes. The authors therefore refined the protocol for inducing hyperglycemia and compared the bacterial burden in controls to pharmacologically induced type 1 diabetic rats after undergoing internal fracture plate fixation and Staphylococcus aureus surgical-site inoculation. Using an initial series of streptozotocin doses, followed by optional additional doses to reach a target blood glucose range of 300 to 600 mg/dl, the authors reliably induced diabetes in 100 percent of the rats (n = 16), in which a narrow hyperglycemic range was maintained 14 days after onset of diabetes (mean ± SEM, 466 ± 16 mg/dl; coefficient of variation, 0.15). With respect to their primary endpoint, the authors quantified a significantly higher infectious burden in inoculated diabetic animals (median, 3.2 × 10 colony-forming units/mg dry tissue) compared with inoculated nondiabetic animals (7.2 × 10 colony-forming units/mg dry tissue). These data support the authors' hypothesis that uncontrolled diabetes adversely affects the immune system's ability to clear Staphylococcus aureus associated with internal hardware.

Perioperative cerebrospinal fluid and plasma inflammatory markers after orthopedic surgery.

BACKGROUND: Postoperative delirium is prevalent in older patients and associated with worse outcomes. Recent data in animal studies demonstrate increases in inflammatory markers in plasma and cerebrospinal fluid (CSF) even after aseptic surgery, suggesting that inflammation of the central nervous system may be part of the pathogenesis of postoperative cognitive changes. We investigated the hypothesis that neuroinflammation was an important cause for postoperative delirium and cognitive dysfunction after major non-cardiac surgery. METHODS: After Institutional Review Board approval and informed consent, we recruited patients undergoing major knee surgery who received spinal anesthesia and femoral nerve block with intravenous sedation. All patients had an indwelling spinal catheter placed at the time of spinal anesthesia that was left in place for up to 24 h. Plasma and CSF samples were collected preoperatively and at 3, 6, and 18 h postoperatively. Cytokine levels were measured using ELISA and Luminex. Postoperative delirium was determined using the confusion assessment method, and cognitive dysfunction was measured using validated cognitive tests (word list, verbal fluency test, digit symbol test). RESULTS: Ten patients with complete datasets were included. One patient developed postoperative delirium, and six patients developed postoperative cognitive dysfunction. Postoperatively, at different time points, statistically significant changes compared to baseline were present in IL-5, IL-6, I-8, IL-10, monocyte chemotactic protein (MCP)-1, macrophage inflammatory protein (MIP)-1α, IL-6/IL-10, and receptor for advanced glycation end products in plasma and in IFN-γ, IL-6, IL-8, IL-10, MCP-1, MIP-1α, MIP-1β, IL-8/IL-10, and TNF-α in CSF. CONCLUSIONS: Substantial pro- and anti-inflammatory activity in the central neural system after surgery was found. If confirmed by larger studies, persistent changes in cytokine levels may serve as biomarkers for novel clinical trials.