Ambulance Emergency Response Optimization in Developing Countries

The lack of emergency medical transportation is viewed as the main barrier to the access of emergency medical care in low and middle-income countries (LMICs). In this paper, we present a robust optimization approach to optimize both the location and routing of emergency response vehicles, accounting for uncertainty in travel times and spatial demand characteristic of LMICs. We traveled to Dhaka, Bangladesh, the sixth largest and third most densely populated city in the world, to conduct field research resulting in the collection of two unique datasets that inform our approach. This data is leveraged to develop machine learning methodologies to estimate demand for emergency medical services in a LMIC setting and to predict the travel time between any two locations in the road network for different times of day and days of the week. We combine our robust optimization and machine learning frameworks with real data to provide an in-depth investigation into three policy-related questions. First, we demonstrate that outpost locations optimized for weekday rush hour lead to good performance for all times of day and days of the week. Second, we find that significant improvements in emergency response times can be achieved by re-locating a small number of outposts and that the performance of the current system could be replicated using only 30% of the resources. Lastly, we show that a fleet of small motorcycle-based ambulances has the potential to significantly outperform traditional ambulance vans. In particular, they are able to capture three times more demand while reducing the median response time by 42% due to increased routing flexibility offered by nimble vehicles on a larger road network. Our results provide practical insights for emergency response optimization that can be leveraged by hospital-based and private ambulance providers in Dhaka and other urban centers in LMICs

Gli3 Is Required for M2 Macrophage Polarization and M2-Mediated Waldenström Macroglobulinemia Growth and Survival

The macrophage polarization paradigm has become at the forefront of cancer research in recent years; however, the effects of this phenomenon have yet to be investigated in Waldenström Macroglobulinemia (WM). WM is an indolent, B-cell lymphoma, characterized by increased IgM production and infiltration of the bone marrow niche by malignant cells. Macrophages may polarize to one of 2 phenotypes: M1, which is inflammatory or M2, which is inhibitory. While macrophage polarization is typically investigated as a singular point in time, it is important to understand that M2-type macrophages can switch to an M1 phenotype, or vice versa, based on environmental changes. M1 macrophages are typically pro-inflammatory and generally have an anti-tumor role (although some cancers have reported an increased presence of M1 macrophages leading to worse clinical outcomes), while M2 macrophages are typically anti-inflammatory and have a pro-tumorigenic role. Understanding which phenotype is prevalent in WM, as well as the mechanism behind the increased tumorigenesis is important for guiding WM research and future therapies. To assess the effects of macrophages on WM cell proliferation, we examined the effects of macrophage polarization using THP-1 cells, CD14+-derived macrophages from peripheral blood mononuclear cells (PBMCs) and bone marrow-derived macrophages (BMDM) from C57BL/6 mice on WM cell growth and viability. WM cells grown in direct co-culture with macrophages exhibited increased proliferation compared to WM cells grown alone. WM cell viability was also enhanced when cells were directly co-cultured with macrophages. To investigate whether M1 or M2 macrophages were responsible for this increased proliferation and viability, we performed a qPCR analysis of macrophages in indirect and direct co-culture with WM cells. We found that WM cells induce a M2 phenotype upon direct co-culture, but this effect was not seen in indirect co-cultures. Additionally, upon the polarization of macrophages towards an M2 phenotype, we observed that the expression of transcription factor GLI3 was increased, indicating a role for GLI3 in macrophages polarization. In previous work, we found that the transcription factor GLI3 plays a role in regulating cytokine expression and secretion in response to LPS stimulation. In previous work, we performed RNA-seq on macrophages derived from mice lacking Gli3 in myeloid cells (M-Gli3-/-) stimulated with or without LPS. Using a generalized linear model in edgeR, we identified 495 genes with significant interaction effects between genotype and LPS treatment. Ingenuity Pathway Analysis of the interaction genes revealed “Inflammatory Response” and “Immune Cell Trafficking” pathways as most significantly enriched. The 25 significant interaction genes on these pathways included 9 with a positive interaction and 16 with a negative interaction. Analysis also suggested Gli3 may play a role in M2 macrophage polarization. Bone marrow-derived macrophages were isolated from M-Gli3-/- and WT mice and were cocultured with WM cells. We found that M-Gli3-/- macrophages could not increase the proliferation and viability of WM cells cocultured with these macrophages. Our findings identify a novel role for Gli3 in regulating M2 polarization and subsequently a role for M0 and M2 macrophages, but not M1, in promoting WM cell growth and survival. Taken together, these results suggest that therapeutic targeting of Gli3 in the tumor microenvironment may be beneficial in the treatment of WM

CP-nets: From Theory to Practice

Conditional preference networks (CP-nets) exploit the power of ceteris paribus rules to represent preferences over combinatorial decision domains compactly. CP-nets have much appeal. However, their study has not yet advanced sufficiently for their widespread use in real-world applications. Known algorithms for deciding dominance---whether one outcome is better than another with respect to a CP-net---require exponential time. Data for CP-nets are difficult to obtain: human subjects data over combinatorial domains are not readily available, and earlier work on random generation is also problematic. Also, much of the research on CP-nets makes strong, often unrealistic assumptions, such as that decision variables must be binary or that only strict preferences are permitted. In this thesis, I address such limitations to make CP-nets more useful. I show how: to generate CP-nets uniformly randomly; to limit search depth in dominance testing given expectations about sets of CP-nets; and to use local search for learning restricted classes of CP-nets from choice data

Relevance Grounding for Planning in Relational Domains

Abstract. Probabilistic relational models are an efficient way to learn and represent the dynamics in realistic environments consisting of many objects. Autonomous intelligent agents that ground this representation for all objects need to plan in exponentially large state spaces and large sets of stochastic actions. A key insight for computational efficiency is that successful planning typically involves only a small subset of relevant objects. In this paper, we introduce a probabilistic model to represent planning with subsets of objects and provide a definition of object relevance. Our definition is sufficient to prove consistency between repeated planning in partially grounded models restricted to relevant objects and planning in the fully grounded model. We propose an algorithm that exploits object relevance to plan efficiently in complex domains. Empirical results in a simulated 3D blocksworld with an articulated manipulator and realistic physics prove the effectiveness of our approach.

Concentration field based micropore flow rate measurements

Demand is growing for a larger catalogue of experimental techniques to measure flow rates through micro-/nanoscale systems for both fundamental research and device development. Flow emerging from a hole in a plane wall is a common system of interest in such work for its relevance to membrane separation. In this paper, we consider the possibility of measuring volume flow rates through small scale orifice plates from images of dye dispersions downstream. Based on approximate analytical solutions to the advection–diffusion equation, we show that, at low Reynolds numbers, the concentration in the nearly hemispherical plume that forms increases linearly with inverse distance from the pore and that the slope is proportional to volume flow rate. From micrographs of fluorescent dye plumes taken downstream of micropores of three different diameters, we demonstrate that, at Reynolds numbers below 15, the volume flow rate can be determined by extracting this slope from fluorescence intensity images. At higher Reynolds numbers, laminar jets form. In this regime, we derive an approximate similarity solution for the concentration field and show agreement of imaged dye dispersion shapes with both analytical expressions for the streamlines and isoconcentration contours at Reynolds numbers above 25. The results validate a scalable method for flow rate measurements applicable to small micropores of any geometry in plane walls and to small areas of porous materials relevant to membrane systems

BABY BOOM target genes provide diverse entry points into cell proliferation and cell growth pathways

Ectopic expression of the Brassica napus BABY BOOM (BBM) AP2/ERF transcription factor is sufficient to induce spontaneous cell proliferation leading primarily to somatic embryogenesis, but also to organogenesis and callus formation. We used DNA microarray analysis in combination with a post-translationally regulated BBM:GR protein and cycloheximide to identify target genes that are directly activated by BBM expression in Arabidopsis seedlings. We show that BBM activated the expression of a largely uncharacterized set of genes encoding proteins with potential roles in transcription, cellular signaling, cell wall biosynthesis and targeted protein turnover. A number of the target genes have been shown to be expressed in meristems or to be involved in cell wall modifications associated with dividing/growing cells. One of the BBM target genes encodes an ADF/cofilin protein, ACTIN DEPOLYMERIZING FACTOR9 (ADF9). The consequences of BBM:GR activation on the actin cytoskeleton were followed using the GFP:FIMBRIN ACTIN BINDING DOMAIN2 (GFP:FABD) actin marker. Dexamethasone-mediated BBM:GR activation induced dramatic changes in actin organization resulting in the formation of dense actin networks with high turnover rates, a phenotype that is consistent with cells that are rapidly undergoing cytoplasmic reorganization. Together the data suggest that the BBM transcription factor activates a complex network of developmental pathways associated with cell proliferation and growth

Sequential Deliberation for Social Choice

In large scale collective decision making, social choice is a normative study of how one ought to design a protocol for reaching consensus. However, in instances where the underlying decision space is too large or complex for ordinal voting, standard voting methods of social choice may be impractical. How then can we design a mechanism - preferably decentralized, simple, scalable, and not requiring any special knowledge of the decision space - to reach consensus? We propose sequential deliberation as a natural solution to this problem. In this iterative method, successive pairs of agents bargain over the decision space using the previous decision as a disagreement alternative. We describe the general method and analyze the quality of its outcome when the space of preferences define a median graph. We show that sequential deliberation finds a 1.208- approximation to the optimal social cost on such graphs, coming very close to this value with only a small constant number of agents sampled from the population. We also show lower bounds on simpler classes of mechanisms to justify our design choices. We further show that sequential deliberation is ex-post Pareto efficient and has truthful reporting as an equilibrium of the induced extensive form game. We finally show that for general metric spaces, the second moment of of the distribution of social cost of the outcomes produced by sequential deliberation is also bounded

Multisite Evaluation of Prediction Models for Emergency Department Crowding Before and During the COVID-19 Pandemic

OBJECTIVE: To develop a machine learning framework to forecast emergency department (ED) crowding and to evaluate model performance under spatial and temporal data drift. MATERIALS AND METHODS: We obtained 4 datasets, identified by the location: 1-large academic hospital and 2-rural hospital, and time period: pre-coronavirus disease (COVID) (January 1, 2019-February 1, 2020) and COVID-era (May 15, 2020-February 1, 2021). Our primary target was a binary outcome that is equal to 1 if the number of patients with acute respiratory illness that were ED boarding for more than 4 h was above a prescribed historical percentile. We trained a random forest and used the area under the curve (AUC) to evaluate out-of-sample performance for 2 experiments: (1) we evaluated the impact of sudden temporal drift by training models using pre-COVID data and testing them during the COVID-era, (2) we evaluated the impact of spatial drift by testing models trained at location 1 on data from location 2, and vice versa. RESULTS: The baseline AUC values for ED boarding ranged from 0.54 (pre-COVID at location 2) to 0.81 (COVID-era at location 1). Models trained with pre-COVID data performed similarly to COVID-era models (0.82 vs 0.78 at location 1). Models that were transferred from location 2 to location 1 performed worse than models trained at location 1 (0.51 vs 0.78). DISCUSSION AND CONCLUSION: Our results demonstrate that ED boarding is a predictable metric for ED crowding, models were not significantly impacted by temporal data drift, and any attempts at implementation must consider spatial data drift

Combinatorial Voter Control in Elections

Voter control problems model situations such as an external agent trying to affect the result of an election by adding voters, for example by convincing some voters to vote who would otherwise not attend the election. Traditionally, voters are added one at a time, with the goal of making a distinguished alternative win by adding a minimum number of voters. In this paper, we initiate the study of combinatorial variants of control by adding voters: In our setting, when we choose to add a voter~$v$, we also have to add a whole bundle $\kappa(v)$ of voters associated with $v$. We study the computational complexity of this problem for two of the most basic voting rules, namely the Plurality rule and the Condorcet rule.Comment: An extended abstract appears in MFCS 201

The Apriori Stochastic Dependency Detection (ASDD) algorithm for learning Stochastic logic rules

Apriori Stochastic Dependency Detection (ASDD) is an algorithm for fast induction of stochastic logic rules from a database of observations made by an agent situated in an environment. ASDD is based on features of the Apriori algorithm for mining association rules in large databases of sales transactions [1] and the MSDD algorithm for discovering stochastic dependencies in multiple streams of data [15]. Once these rules have been acquired the Precedence algorithm assigns operator precedence when two or more rules matching the input data are applicable to the same output variable. These algorithms currently learn propositional rules, with future extensions aimed towards learning first-order models. We show that stochastic rules produced by this algorithm are capable of reproducing an accurate world model in a simple predator-prey environment