Applying Scenario Reduction Heuristics in Stochastic Programming for Phlebotomist Scheduling

Laboratory services in healthcare play a vital role in inpatient care. Studies have indicated laboratory data affect approximately 65% of the most critical decisions on admission, discharge, and medication. This research focuses on improving phlebotomist performance in laboratory facilities of large hospital systems. A two-stage stochastic integer linear programming (SILP) model is formulated to determine better weekly phlebotomist schedules and blood collection assignments. The objective of the two-stage SILP model is to balance the workload of the phlebotomists within and between shifts, as reducing workload imbalance will result in improved patient care. Due to the size of the two-stage SILP model, a scenario reduction model has been proposed as a solution approach. The scenario reduction heuristic is formulated as a linear programming model and the results indicate the scenarios with the largest likelihood of occurrence. These selected scenarios will be tested in the two-stage SILP model to determine weekly scheduling policies and blood draw assignments that will balance phlebotomist workload and improve overall performance

Teaching Cybersecurity in an Undergraduate Engineering Course

Organizations create a huge amount of sensitive and confidential data, which must be protected from unauthorized access or disclosure. Nowadays, most organizations store their business data in digital formats. With the increasing use of digital data, data breaches are more often and serious in recent years. Therefore, it is very important for next-generation engineers to be aware of the importance of information security, and be able to recognize vulnerabilities and threats to an information system and design user-friendly and effective security measures. To achieve it, two modules of information systems security, including lectures and in-class labs, were developed and taught in an undergraduate engineering course at North Carolina A&T State University. The learning objectives, teaching materials, and assessment outcomes of the two course modules are presented in this paper. Our survey results show that the course modules achieve the learning objectives and improve students’ interest in pursuing cybersecurity-related careers. Keywords: Engineering Education, Database Security, Usable and Effective Securit

Multi-Scale Models for Transportation Systems Under Emergency Conditions

In recent years natural disasters have caused significant disruptions to transportation systems, which had to cascade negative impacts on humanitarian operations, related infrastructure, and associated industries in the affected areas. How to prepare for and respond to transportation system disruptions is a complex decision incorporating a variety of factors, from system use to system preparation. To address these challenges, the project team has developed optimization models for flight rescheduling and road restoration after a natural disaster and integrated the models as a decisionmaking tool. The data of North Carolina emergency response activities, air flights, and road closures during Hurricane Matthew were used to test the models and tool. The testing results show that the integrated tool can quickly find optimal sets and sequences for road restoration and flight schedules recovery at an affected airport and 50 counties. The tool can also visualize the damaged connections between counties, airports and resource centers, and the road restoration schedule and flight schedules recovery plan. The optimization models and decision-making tool developed in this project can support deploying effective restoration and recovery of transportation systems during an emergency event, which can improve the mobility of people and disaster relief under emergency

The adenosine A2A receptor antagonist KW6002 distinctly regulates retinal ganglion cell morphology during postnatal development and neonatal inflammation

Adenosine A2A receptors (A2ARs) appear early in the retina during postnatal development, but the roles of the A2ARs in the morphogenesis of distinct types of retinal ganglion cells (RGCs) during postnatal development and neonatal inflammatory response remain undetermined. As the RGCs are rather heterogeneous in morphology and functions in the retina, here we resorted to the Thy1-YFPH transgenic mice and three-dimensional (3D) neuron reconstruction to investigate how A2ARs regulate the morphogenesis of three morphologically distinct types of RGCs (namely Type I, II, III) during postnatal development and neonatal inflammation. We found that the A2AR antagonist KW6002 did not change the proportion of the three RGC types during retinal development, but exerted a bidirectional effect on dendritic complexity of Type I and III RGCs and cell type-specifically altered their morphologies with decreased dendrite density of Type I, decreased the dendritic field area of Type II and III, increased dendrite density of Type III RGCs. Moreover, under neonatal inflammation condition, KW6002 specifically increased the proportion of Type I RGCs with enhanced the dendrite surface area and volume and the proportion of Type II RGCs with enlarged the soma area and perimeter. Thus, A2ARs exert distinct control of RGC morphologies to cell type-specifically fine-tune the RGC dendrites during normal development but to mainly suppress RGC soma and dendrite volume under neonatal inflammation

Morphological diversity of single neurons in molecularly defined cell types.

Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types1,2, yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits

A multimodal cell census and atlas of the mammalian primary motor cortex

ABSTRACT We report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties, and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Together, our results advance the collective knowledge and understanding of brain cell type organization: First, our study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a unified taxonomy of transcriptomic types and their hierarchical organization that are conserved from mouse to marmoset and human. Third, cross-modal analysis provides compelling evidence for the epigenomic, transcriptomic, and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types and subtypes. Fourth, in situ single-cell transcriptomics provides a spatially-resolved cell type atlas of the motor cortex. Fifth, integrated transcriptomic, epigenomic and anatomical analyses reveal the correspondence between neural circuits and transcriptomic cell types. We further present an extensive genetic toolset for targeting and fate mapping glutamatergic projection neuron types toward linking their developmental trajectory to their circuit function. Together, our results establish a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties

Development of appointment scheduling rules for open access scheduling

In the early 1990s, a new appointment scheduling concept for outpatient clinics, labeled open access scheduling, advanced access scheduling, or same-day scheduling, was introduced to improve quality of healthcare and reduce the cost of healthcare delivery. The concept becomes popular in medical practices nowadays because it addresses two of the six key aims for healthcare quality improvement targeted by Institute of Medicine: timely care and patient-centered care. However, this concept, still under development, is far from mature, and the critical parameters in open access scheduling are still determined by experience rather than quantitative methods. In this research, mathematical models and quantitative procedures are developed to optimize critical parameters in open access scheduling, and the general guidelines to choose appropriate parameters are obtained by investigating the impacts of clinic environmental characteristics on the optimal parameters. The critical parameters optimized include the percentage of open-access appointments, the time horizon of open-access appointments, and a scheduling template for each provider in each clinic session. First, several efficient quantitative procedures are developed to determine the optimal or Pareto optimal percentages of prescheduled and open-access appointments for a provider in a clinic session. Then, by combining tabu search and Ranking and Selection approach, a heuristic procedure is developed to optimally allocate appointment slots to prescheduled and open-access appointments for a provider in a session. Using these procedures, we investigate the impacts of time horizon of open-access appointments and clinic environmental characteristics on the performance of open access scheduling, and then summarize the general guidelines for clinic administrators to choose a best scheduling policy for each provider in each clinic session in the clinics implementing open access scheduling. These quantitative approaches and general guidelines can decrease the probability of failure to implement open access scheduling, and improve the performance of open access scheduling

Real‐Time Recommendations for Traffic Control in an Intelligent Transportation System during an Emergency Evacuation

Computer and communication technologies (ICT) have been incorporated in the NC transportation infrastructure to build intelligent transportation systems (ITSs), which provide us opportunities to improve the effectiveness and efficiency of emergency response. As part of natural disaster preparation and response, evacuations often occur before or after natural disasters such as hurricanes and earthquakes. Recent hurricanes such as Irma (2017) and Florence (2018) caused mass evacuations and brought the public and research communities\u27 attentions to many issues in evacuation plans implemented. It is obvious that effective and proper traffic control is crucial during a mass evacuation. During natural disasters, ITSs can play an important role in mass emergency evacuations. In this project, we propose to develop and integrate ecological models for human evacuation behavior prediction and a real‐time traffic control recommendation system to support disaster evacuations in intelligent transportation infrastructure. Optimization, simulation and machine learning approaches will be used to develop the proposed system

Effect of two-level provider capacities on the performance of open access clinics

Open access, Appointment scheduling rules, Provider capacity, Performance metrics, Outpatient clinic,