Jyoti Kamal

Dependence of ground heat loss upon solar pond size and perimeter insulation: calculated and experimental results

Abstract Ground heat losses from solar ponds are modelled numerically for various perimeter insulation strategies and several solar pond sizes. The numerical simulations are steady state calculations of heat loss from a circular or square pond to a heat sink at the outer boundaries of an earth volume that surrounds the pond on the bottom and sides. Simulation results indicate that insulation on top of the ground around the pond perimeter is rather ineffective in reducing heat loss, and that uninsulated sloping side walls are slightly more effective than insulated vertical side walls, except for very small ponds. The numerical results are used to derive coefficients for a semi-empirical equation describing ground heat loss as a function of pond area, pond perimeter and insulation strategy. Experimental results for ground heat loss and energy balance in the 400 m 2 solar pond at the Ohio State University are reported. Analysis of this data, along with data on solar energy input, heat gain by the pond, heat loss through the gradient zone, and heat extraction from the pond yields a good energy balance. Numerical simulation of ground heat loss from this pond shows good agreement with the results obtained from pond measurements. Loss turns out to be large because of unexpectedly high values of earth thermal conductivity in the region.

Aberrant expression of DNA damage response proteins is associated with breast cancer subtype and clinical features

Landmark studies of the status of DNA damage checkpoints and associated repair functions in preneoplastic and neoplastic cells has focused attention on importance of these pathways in cancer development, and inhibitors of repair pathways are in clinical trials for treatment of triple negative breast cancer. Cancer heterogeneity suggests that specific cancer subtypes will have distinct mechanisms of DNA damage survival, dependent on biological context. In this study, status of DNA damage response (DDR)-associated proteins was examined in breast cancer subtypes in association with clinical features; 479 breast cancers were examined for expression of DDR proteins γH2AX, BRCA1, pChk2, and p53, DNA damage-sensitive tumor suppressors Fhit and Wwox, and Wwox-interacting proteins Ap2α, Ap2γ, ErbB4, and correlations among proteins, tumor subtypes, and clinical features were assessed. In a multivariable model, triple negative cancers showed significantly reduced Fhit and Wwox, increased p53 and Ap2γ protein expression, and were significantly more likely than other subtype tumors to exhibit aberrant expression of two or more DDR-associated proteins. Disease-free survival was associated with subtype, Fhit and membrane ErbB4 expression level and aberrant expression of multiple DDR-associated proteins. These results suggest that definition of specific DNA repair and checkpoint defects in subgroups of triple negative cancer might identify new treatment targets. Expression of Wwox and its interactor, ErbB4, was highly significantly reduced in metastatic tissues vs. matched primary tissues, suggesting that Wwox signal pathway loss contributes to lymph node metastasis, perhaps by allowing survival of tumor cells that have detached from basement membranes, as proposed for the role of Wwox in ovarian cancer spread.

A Knowledge-Anchored Integrative Image Search and Retrieval System

Clinical data that may be used in a secondary capacity to support research activities are regularly stored in three significantly different formats: (1) structured, codified data elements; (2) semi-structured or unstructured narrative text; and (3) multi-modal images. In this manuscript, we will describe the design of a computational system that is intended to support the ontology-anchored query and integration of such data types from multiple source systems. Additional features of the described system include (1) the use of Grid services-based electronic data interchange models to enable the use of our system in multi-site settings and (2) the use of a software framework intended to address both potential security and patient confidentiality concerns that arise when transmitting or otherwise manipulating potentially privileged personal health information. We will frame our discussion within the specific experimental context of the concept-oriented query and integration of correlated structured data, narrative text, and images for cancer research.

The Glucogram: A New Quantitative Tool for Glycemic Analysis in the Surgical Intensive Care Unit

Background: Glycemic control is an important aspect of patient care in the surgical intensive care unit (SICU). This is a pilot study of a novel glycemic analysis tool – the glucogram. We hypothesize that the glucogram may be helpful in quantifying the clinical significance of acute hyperglycemic states (AHS) and in describing glycemic variability (GV) in critically ill patients. Materials and Methods: Serial glucose measurements were analyzed in SICU patients with lengths of stay (LOS) >30 days. Glucose data were formatted into 12-hour epochs and graphically analyzed using stochastic and momentum indicators. Recorded clinical events were classified as major or minor (control). Examples of major events include cardiogenic shock, acute respiratory failure, major hemorrhage, infection/sepsis, etc. Examples of minor (control) events include non-emergent bedside procedures, blood transfusion given to a hemodynamically stable patient, etc. Positive/negative indicator status was then correlated with AHS and associated clinical events. The conjunction of positive indicator/major clinical event or negative indicator/minor clinical event was defined as clinical “match”. GV was determined by averaging glucose fluctuations (maximal – minimal value within each 12-hour epoch) over time. In addition, event-specific glucose excursion (ESGE) associated with each positive indicator/AHS match (final minus initial value for each occurrence) was calculated. Descriptive statistics, sensitivity/specificity determination, and students t-test were used in data analysis. Results: Glycemic and clinical data were reviewed for 11 patients (mean SICU LOS 74.5 days; 7 men/4 women; mean age 54.9 years; APACHE II of 17.7 ± 6.44; mortality 36%). A total of 4354 glucose data points (1254 epochs) were analyzed. There were 354 major clinical events and 93 minor (control) events. The glucogram identified AHS/indicator/clinical event “matches” with overall sensitivity of 84% and specificity of 65%. We noted that while the mean GV was greater for non-survivors than for survivors (19.3 mg/dL vs. 10.3 mg/dL, P = 0.02), there was no difference in mean ESGE between survivors (154.7) and non-survivors (160.8, P = 0.67). Conclusions: The glucogram was able to quantify the correlation between AHS and major clinical events with a sensitivity of 84% and a specificity of 65%. In addition, mean GV was nearly two times higher for non-survivors. The glucogram may be useful both clinically (i.e., in the electronic ICU or other “early warning” systems) and as a research tool (i.e., in model development and standardization). Results of this study provide a foundation for further, larger-scale, multi-parametric, prospective evaluations of the glucogram.