Efficient Algorithms for Identifying Loop Formation and Computing θ Value for Solving Minimum Cost Flow Network Problems

While the minimum cost flow (MCF) problems have been well documented in many publications, due to its broad applications, little or no effort have been devoted to explaining the algorithms for identifying loop formation and computing the value needed to solve MCF network problems. This paper proposes efficient algorithms, and MATLAB computer implementation, for solving MCF problems. Several academic and real-life network problems have been solved to validate the proposed algorithms; the numerical results obtained by the developed MCF code have been compared and matched with the built-in MATLAB function Linprog() (Simplex algorithm) for further validation

Arriving on time: estimating travel time distributions on large-scale road networks

Most optimal routing problems focus on minimizing travel time or distance traveled. Oftentimes, a more useful objective is to maximize the probability of on-time arrival, which requires statistical distributions of travel times, rather than just mean values. We propose a method to estimate travel time distributions on large-scale road networks, using probe vehicle data collected from GPS. We present a framework that works with large input of data, and scales linearly with the size of the network. Leveraging the planar topology of the graph, the method computes efficiently the time correlations between neighboring streets. First, raw probe vehicle traces are compressed into pairs of travel times and number of stops for each traversed road segment using a `stop-and-go' algorithm developed for this work. The compressed data is then used as input for training a path travel time model, which couples a Markov model along with a Gaussian Markov random field. Finally, scalable inference algorithms are developed for obtaining path travel time distributions from the composite MM-GMRF model. We illustrate the accuracy and scalability of our model on a 505,000 road link network spanning the San Francisco Bay Area

Evaluating Innovative Financing Mechanisms for the California High-Speed Rail Project

Millions of dollars are involved in high-speed rail (HSR) infrastructure construction and maintenance. Large-scale projects like HSR require funding from a variety of avenues beyond those available through public monies. Although HSR serves the general public’s mobility needs, any funds (whether State or Federal) flowing from the public exchequer usually undergo strict review and scrutiny. Funds from public agencies are always limited, making such traditional financing mechanisms unsustainable for fulfilling HSR’s long-term operational and maintenance cost needs—on top of initial costs involved in construction. Therefore, any sustainable means of financing HSR projects would always be welcome. This research presents an alternate revenue generation mechanism that could be sustainable for financing HSR’s construction, operation, and maintenance. The methodology involves determining key HSR stations, which, after development and improvement, could significantly add value to businesses and real estate growth. Any form of real estate taxes levied on properties surrounding such stations could substantially support the HSR project’s funding needs. In this research, a bi-objective optimization problem is posed in conjunction with a Pareto-optimal front framework to identify those key stations. With 28 California HSR stations used as an example, it was observed that the four proposed HSR stations in Fullerton, Millbrae-SFO, San Francisco Transbay Terminal, and San Diego would be excellent candidates for development. Their development could increase the economic vitality of surrounding businesses. The findings could serve as valuable information for California HSR authorities to focus on developing key stations that would generate an alternate funding source for an HSR project facing funding challenges

Evaluating Financing Mechanisms and Economic Benefits to Fund Grade Separation Projects

Investment in transportation infrastructure projects generates benefits, both direct and indirect. While emissions reductions, crash reductions, and travel time savings are prominent direct benefits, there are indirect benefits in the form of real estate enhancements that could pay off debt or loan incurred in the improvement of the infrastructure itself. Studies have shown that improvements associated with rail transportation (such as station upgrades) trigger an increase in the surrounding real estate values, increasing both the opportunity for monetary gains and, ultimately, property tax collections. There is plenty of available guidance that provides blueprints for benefits calculations for operational improvements in rail transportation. However, resources are quite limited in the analysis of benefits that accrue from the separation of railroad at-grade crossings. Understanding the impact of separation in a neighborhood with high employment or population could generate revenues through increased tax collections. In California, the research need is further amplified by a lack of guidance from the California Public Utilities Commission (CPUC) on at-grade crossing for separation based on revenue generated. There is a critical need to understand whether grade separation projects could impact neighboring real estate values that could potentially be used to fund such separations. With COVID-19, as current infrastructure spending in California is experiencing a reboot, an approach more oriented to benefits and costs for railroad at-grade separation should be explored. Thus, this research uses a robust benefits-to-cost analysis (BCA) to probe the economic impacts of railroad at-grade separation projects. The investigation is carried out across twelve railroad-highway at-grade crossings in California. These crossings are located at Francisquito Ave., Willowbrook/Rosa Parks Station, Sassafras St., Palm St., Civic Center Dr., L St., Spring St. (North), J St., E St., H St., Parkmoor West, and Nursery Ave. The authors found that a majority of the selected at-grade crossings analyzed accrue high benefits-to-cost (BC) ratios from travel time savings, safety improvements, emissions reductions, and potential revenue generated if property taxes are collected and used to fund such separation projects. The analysis shows that with the estimated BC ratios, the railroad crossing at Nursery Ave. in Fremont, Palm St. in San Diego, and H St. in Chula Vista could be ideal candidates for separation. The methodology presented in this research could serve as a handy reference for decision-makers selecting one or more at-grade crossings for the separation considering economic outputs and costs

The role of the lateral prefrontal cortex and anterior cingulate in stimulus–response association reversals

Many complex tasks require us to flexibly switch between behavioral rules, associations, and strategies. The prefrontal cerebral cortex is thought to be critical to the performance of such behaviors, although the relative contribution of different components of this structure and associated subcortical regions are not fully understood. We used functional magnetic resonance imaging to measure brain activity during a simple task which required repeated reversals of a rule linking a colored cue and a left/right motor response. Each trial comprised three discrete events separated by variable delay periods. A colored cue instructed which response was to be executed, followed by a go signal which told the subject to execute the response and a feedback instruction which indicated whether to ‘‘hold’’ or ‘‘f lip’’ the rule linking the colored cue and response. The design allowed us to determine which brain regions were recruited by the specific demands of preparing a rule contingent motor response, executing such a response, evaluating the significance of the feedback, and reconfiguring stimulus–response (SR) associations. The results indicate that an increase in neural activity occurs within the anterior cingulate gyrus under conditions in which SR associations are labile. In contrast, lateral frontal regions are activated by unlikely/unexpected perceptual events regardless of their significance for behavior. A network of subcortical structures, including the mediodorsal nucleus of the thalamus and striatum were the only regions showing activity that was exclusively correlated with the neurocognitive demands of reversing SR associations. We conclude that lateral frontal regions act to evaluate the behavioral significance of perceptual events, whereas medial frontal–thalamic circuits are involved in monitoring and reconfiguring SR associations when necessary

Sources of Multidrug Resistance in Patients With Previous Isoniazid-Resistant Tuberculosis Identified Using Whole Genome Sequencing: A Longitudinal Cohort Study

Background Meta-analysis of patients with isoniazid-resistant tuberculosis given standard first-line anti-tuberculosis treatment indicated an increased risk of multi-drug resistant tuberculosis (MDR-TB) emerging (8%), compared to drug-sensitive tuberculosis (0.3%). Here we use whole genome sequencing (WGS) to investigate whether treatment of patients with pre-existing isoniazid resistant disease with first-line anti-tuberculosis therapy risks selecting for rifampicin resistance, and hence MDR-TB. Methods Patients with isoniazid-resistant pulmonary TB were recruited and followed up for 24 months. Drug-susceptibility testing was performed by Microscopic observation drug-susceptibility assay (MODS), Mycobacterial Growth Indicator Tube (MGIT) and by WGS on isolates at first presentation and in the case of re-presentation. Where MDR-TB was diagnosed, WGS was used to determine the genomic relatedness between initial and subsequent isolates. De novo emergence of MDR-TB was assumed where the genomic distance was five or fewer single nucleotide polymorphisms (SNPs) whereas reinfection with a different MDR-TB strain was assumed where the distance was 10 or more SNPs. Results 239 patients with isoniazid-resistant pulmonary tuberculosis were recruited. Fourteen (14/239, 5.9%) patients were diagnosed with a second episode of tuberculosis that was multi-drug resistant. Six (6/239, 2.5%) were identified as having evolved MDR-TB de novo and six as having been re-infected with a different strain. In two cases the genomic distance was between 5-10 SNPs and therefore indeterminate. Conclusions In isoniazid-resistant TB, de novo emergence and reinfection of MDR-TB strains equally contributed to MDR development. Early diagnosis and optimal treatment of isoniazid resistant TB are urgently needed to avert the de novo emergence of MDR-TB during treatment

Tumor innate immunity primed by specific interferon-stimulated endogenous retroviruses.

Mesenchymal tumor subpopulations secrete pro-tumorigenic cytokines and promote treatment resistance1-4. This phenomenon has been implicated in chemorefractory small cell lung cancer and resistance to targeted therapies5-8, but remains incompletely defined. Here, we identify a subclass of endogenous retroviruses (ERVs) that engages innate immune signaling in these cells. Stimulated 3 prime antisense retroviral coding sequences (SPARCS) are oriented inversely in 3' untranslated regions of specific genes enriched for regulation by STAT1 and EZH2. Derepression of these loci results in double-stranded RNA generation following IFN-γ exposure due to bi-directional transcription from the STAT1-activated gene promoter and the 5' long terminal repeat of the antisense ERV. Engagement of MAVS and STING activates downstream TBK1, IRF3, and STAT1 signaling, sustaining a positive feedback loop. SPARCS induction in human tumors is tightly associated with major histocompatibility complex class 1 expression, mesenchymal markers, and downregulation of chromatin modifying enzymes, including EZH2. Analysis of cell lines with high inducible SPARCS expression reveals strong association with an AXL/MET-positive mesenchymal cell state. While SPARCS-high tumors are immune infiltrated, they also exhibit multiple features of an immune-suppressed microenviroment. Together, these data unveil a subclass of ERVs whose derepression triggers pathologic innate immune signaling in cancer, with important implications for cancer immunotherapy

NASA SpaceCube Next-Generation Artificial-Intelligence Computing for STP-H9-SCENIC on ISS

Recently, Artificial Intelligence (AI) and Machine Learning (ML) capabilities have seen an exponential increase in interest from academia and industry that can be a disruptive, transformative development for future missions. Specifically, AI/ML concepts for edge computing can be integrated into future missions for autonomous operation, constellation missions, and onboard data analysis. However, using commercial AI software frameworks onboard spacecraft is challenging because traditional radiation-hardened processors and common spacecraft processors cannot provide the necessary onboard processing capability to effectively deploy complex AI models. Advantageously, embedded AI microchips being developed for the mobile market demonstrate remarkable capability and follow similar size, weight, and power constraints that could be imposed on a space-based system. Unfortunately, many of these devices have not been qualified for use in space. Therefore, Space Test Program - Houston 9 - SpaceCube Edge-Node Intelligent Collaboration (STP-H9-SCENIC) will demonstrate inflight, cutting-edge AI applications on multiple space-based devices for next-generation onboard intelligence. SCENIC will characterize several embedded AI devices in a relevant space environment and will provide NASA and DoD with flight heritage data and lessons learned for developers seeking to enable AI/ML on future missions. Finally, SCENIC also includes new CubeSat form-factor GPS and SDR cards for guidance and navigation