Reducing False Discoveries in Statistically-Significant Regional-Colocation Mining: A Summary of Results

Given a set S of spatial feature types, its feature instances, a study area, and a neighbor relationship, the goal is to find pairs such that C is a statistically significant regional-colocation pattern in r_{g}. This problem is important for applications in various domains including ecology, economics, and sociology. The problem is computationally challenging due to the exponential number of regional colocation patterns and candidate regions. Previously, we proposed a miner [Subhankar et. al, 2022] that finds statistically significant regional colocation patterns. However, the numerous simultaneous statistical inferences raise the risk of false discoveries (also known as the multiple comparisons problem) and carry a high computational cost. We propose a novel algorithm, namely, multiple comparisons regional colocation miner (MultComp-RCM) which uses a Bonferroni correction. Theoretical analysis, experimental evaluation, and case study results show that the proposed method reduces both the false discovery rate and computational cost

Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-variability-aware Approach

Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change's impacts on polar ice sheets and the ocean. This problem is challenging due to spatial variability and unknowns such as possible tipping points (e.g., collapse of Greenland or West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost thawing), future policy decisions, and human actions. Most existing climate modeling approaches use the same set of weights globally, during either regression or deep learning to combine different climate projections. Such approaches are inadequate when different regions require different weighting schemes for accurate and reliable sea-level rise predictions. This paper proposes a zonal regression model which addresses spatial variability and model inter-dependency. Experimental results show more reliable predictions using the weights learned via this approach on a regional scale.Comment: 6 pages, 5 figures, I-GUIDE 2023 conferenc

Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-Variability-Aware Approach

Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change\u27s impacts on polar ice sheets and the ocean. This problem is challenging due to spatial variability and unknowns such as possible tipping points (e.g., collapse of Greenland or West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost thawing), future policy decisions, and human actions. Most existing climate modeling approaches use the same set of weights globally, during either regression or deep learning to combine different climate projections. Such approaches are inadequate when different regions require different weighting schemes for accurate and reliable sea-level rise predictions. This paper proposes a zonal regression model which addresses spatial variability and model inter-dependency. Experimental results show more reliable predictions using the weights learned via this approach on a regional scale

Serum Neopterin Levels among Hepatitis C-Positive Living-Donor Renal Transplant Recipients

Background The role of neopterin as a marker of cell-mediated immunity for immunological monitoring after transplantation is of great potential interest. Neopterin levels among hepatitis C virus (HCV)-positive recipients of living-donor renal transplantation (LDRT) have not been previously described. Methods Twenty-two HCV-positive (group I) and 10 HCV-negative (group II) recipients of LDRT were serially monitored for serum neopterin levels by enzyme-linked immunosorbent assay (ELISA). Group I patients were monitored thrice, ie, before transplantation, day 10, and 6 months post transplantation, while group II patients were monitored twice (day 10 and 6 months post transplantation). Peripheral blood T-lymphocyte subsets (CD3, CD4, CD8, CD4 + CD25 + , CD 16+56 ) and Thl/Th2 cytokines were monitored concomitantly by flow cytometry. Results Ten days post transplantation, there was a significant increase in neopterin and neopterin/creatnine levels among group I patients. There was a positive correlation between activated T-lymphocyte (CD4 + CD25 + ) and neopterin early post transplantation (day 10). Th2 cytokines IL-10 and IL-5 showed a positive correlation with neopterin levels on day 10 and 6 months post transplantation, respectively. Neopterin levels did not show association with either HCV viral load or allograft rejection among our study cohort. Conclusion Increased monocyte/macrophage activation with elevated serum neopterin was detected among group I patients on day 10 post transplantation, but it could not predict rejection. It appears that IL-10 either from a regulatory or nonregulatory source helps in the maintenance of stable graft early post transplantation. Further, it would be of interest to assess the role of neopterin in chronic allograft nephropathy and long-term graft outcome

A Small Molecule BH3-mimetic Suppresses Cigarette Smoke-Induced Mucous Expression in Airway Epithelial Cells

Abstract Cigarette smoke (CS) exposure is one of the primary risk factors associated with the chronic mucous hypersecretion (CMH). The antiapoptotic protein, Bcl-2 sustains hyperplastic mucous cells, and the airway epithelium of ex-smokers with CMH as well as mice exposed to chronic CS showed increased Bcl-2 expression. Therefore, we investigated whether Bcl-2 plays a role in CS-induced mucous expression. Primary airway epithelial cells (AECs) of murine and human origin were treated with CS extract (CSE), and there was a concentration- and time-dependent increase in secretory mucin (MUC5AC), mucous regulator (SPDEF) and Bcl-2 expression. Using differentiated human AECs cultured on air-liquid interface, EGFR and ERK1/2 pathways were interrogated. Bcl-2 activity was blocked using a small molecule BH3 mimetic ABT-263 that disrupts the Bcl-2 interaction with pro-apoptotic proteins. The ABT-263 treatment resulted in the downregulation of CSE-induced mucus expression and disrupted the EGFR-signaling while inducing the apoptosis and the pro-apoptotic protein, Bik expression. This strategy significantly suppressed the mainstream CS-induced mucous phenotype in a 3-D human airway epithelium model. Therefore, the present study suggests that CS induces Bcl-2 expression to help promote mucous cell survival; and small molecule BH3 mimetics targeting Bcl-2 could be useful in suppressing the CS-induced mucous response