Union Pacific/Southern Pacific merger: impact on shippers

In the Summer of ‘96, Union Pacific Railroad merged with Southern Pacific to create the largest American railroad. Controversy continues to surround the merger. This paper reports results of a recent merger-impact survey. Survey respondents were rail and intermodal shippers. Among the interesting research findings are the following: (1) while shippers report a negative impact due to less rail competition, trackage rights granted to Burlington Northern/Santa Fe have failed to dampen this impact; (2) railroad service has deteriorated, but freight rates have remained stable; and (3) service problems are more severe for rail, as opposed to TOFC/COFC, shippers

Challenges and Solutions for Functional Neurosurgery in Developing Countries.

Functional neurosurgery techniques remain integral to the neurosurgical treatment armamentarium but data on global implementation remains scarce. In comparison to high-income countries (HIC), low- and middle-income countries (LMIC) suffer from an increased prevalence of diseases like epilepsy, which may be amenable to functional techniques, and therefore, LMIC may benefit from an increased utilization of these treatment modalities. However, functional techniques tend to be expensive and thus difficult to implement in the LMIC setting. A review was performed to assess the current status of functional neurosurgical techniques in LMIC as a starting point for future initiatives. For methodology, a review of the current body of literature on functional neurosurgery in LMIC was conducted through the United States National Library of Medicine Pubmed search engine. Search terms included "functional neurosurgery," "developing countries," "low and middle income," and other related terms. It was found that though five billion people lack access to safe surgical care, the burden of disease amenable to treatment with functional neurosurgical procedures remains unknown. Increasingly, reports of successful, long-term, international neurosurgical collaborations are being reported, but reports in the sub-field of functional neurosurgery are lacking. In conclusion, awareness of global surgical disparities has increased dramatically while global guidelines for functional techniques are currently lacking. A concerted effort can harness these techniques for wider practice. Partnerships between centers in LMIC and HIC are making progress to better understand the burden of disease in LMIC and to create context-specific solutions for practice in the LMIC setting, but more collaborations are warranted

Relationships between inventory, sales and service in a retail chain store operation

Effective inventory management is critical to retailing success. Surprisingly, there islittle published empirical research examining relationships between retail inventory, sales andcustomer service. Based on a survey of 101 chain store units, this paper develops and tests aseries of hypotheses about retail inventory. Seventy-five percent of the store owners/managersresponded to the mail survey. As expected, significant positive relationships were found betweeninventory, service and sales. Specifically, support was found for the theory that inventory is afunction of the square root of sales. Also, greater product variety leads to higher inventory, andservice level is an exponential function of inventory. Finally, demand uncertainty was found tohave no apparent effect on inventory levels

Historical Prediction Modeling Approach for Estimating Long-Term Concentrations of PM in Cohort Studies Before the 1999 Implementation of Widespread Monitoring

Introduction: Recent cohort studies use exposure prediction models to estimate the association between long-term residential concentrations of PM2.5 and health. Because these prediction models rely on PM2.5 monitoring data, predictions for times before extensive spatial monitoring present a challenge to understanding long-term exposure effects. The Environmental Protection Agency (EPA) Federal Reference Method (FRM) network for PM2.5 was established in 1999. We evaluated a novel statistical approach to produce high quality exposure predictions from 1980-2010 for epidemiological applications. Methods: We developed spatio-temporal prediction models using geographic predictors and annual average PM2.5 data from 1999 through 2010 from the FRM and the Interagency Monitoring of Protected Visual Environments (IMPROVE) networks. The model consists of a spatially-varying long-term mean, a spatially-varying temporal trend, and spatially-varying and temporally-independent spatio-temporal residuals structured using a universal kriging framework. Temporal trends in annual averages of PM2.5 before 1999 were estimated by using a) extrapolation based on PM2.5 data for 1999-2010 in FRM/IMPROVE, b) PM2.5 sulfate data for 1987-2010 in the Clean Air Status and Trends Network, and c) visibility data for 1980-2010 across the Weather-Bureau-Army-Navy network. We validated the resulting models using PM2.5 data collected before 1999 from IMPROVE, California Air Resources Board dichotomous sampler monitoring (CARB dichot), the Southern California Children’s Health Study (CHS), and the Inhalable Particulate Network (IPN). Results: The PM2.5 prediction model performed well across three trend estimation approaches when validated using IMPROVE and CHS data (R2= 0.84–0.91). Model performance using CARB dichot and IPN data was worse than those in IMPROVE most likely due to inconsistent sampling methods and smaller numbers of monitoring sites. Discussion: Our prediction modeling approach will allow health effects estimation associated with long-term exposures to PM2.5 over extended time periods of up to 30 years

A Flexible Spatio-Temporal Model for Air Pollution: Allowing for Spatio-Temporal Covariates

Given the increasing interest in the association between exposure to air pollution and adverse health outcomes, the development of models that provide accurate spatio-temporal predictions of air pollution concentrations at small spatial scales is of great importance when assessing potential health effects of air pollution. The methodology presented here has been developed as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), a prospective cohort study funded by the US EPA to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. We present a spatio-temporal framework that models and predicts ambient air pollution by combining data from several different monitoring networks with the output from deterministic air pollution model(s). The model can accommodate arbitrarily missing observations and allows for a complex spatio-temporal correlation structure. We apply the model to predict long-term average concentrations of gaseous oxides of nitrogen (NOx) ─ one of the primary pollutants of interest in the MESA Air study ─ during a ten year period in the Los Angeles area, based on measurements from the EPA Air Quality System and MESA Air monitoring. The measurements are augmented by a spatio-temporal covariate based on the output from a source dispersion model for traffic related air pollution (Caline3QHC) and the model is evaluated using cross-validation. The predictive ability of the model is good with cross-validated R2 of approximately 0.7 at subject sites. The incorporation of a dispersion model output into the overall prediction model was feasible, but the particular implementation of Caline3QHC used here did not improve predictions in a model that also includes road information. However, excluding the road information the inclusion of model output improves predictions and we find some evidence that the source dispersion model can replace road covariates. The model presented in this paper has been implemented in an R package, SpatioTemporal, which will be available on CRAN shortly

Magnetic resonance imaging-guided phase 1 trial of putaminal AADC gene therapy for Parkinson's disease.

ObjectiveTo understand the safety, putaminal coverage, and enzyme expression of adeno-associated viral vector serotype-2 encoding the complementary DNA for the enzyme, aromatic L-amino acid decarboxylase (VY-AADC01), delivered using novel intraoperative monitoring to optimize delivery.MethodsFifteen subjects (three cohorts of 5) with moderately advanced Parkinson's disease and medically refractory motor fluctuations received VY-AADC01 bilaterally coadministered with gadoteridol to the putamen using intraoperative magnetic resonance imaging (MRI) guidance to visualize the anatomic spread of the infusate and calculate coverage. Cohort 1 received 8.3 × 1011 vg/ml and ≤450 μl per putamen (total dose, ≤7.5 × 1011 vg); cohort 2 received the same concentration (8.3 × 1011 vg/ml) and ≤900 μl per putamen (total dose, ≤1.5 × 1012 vg); and cohort 3 received 2.6 × 1012 vg/ml and ≤900 μl per putamen (total dose, ≤4.7 × 1012 vg). (18)F-fluoro-L-dihydroxyphenylalanine positron emission tomography (PET) at baseline and 6 months postprocedure assessed enzyme activity; standard assessments measured clinical outcomes.ResultsMRI-guided administration of ascending VY-AADC01 doses resulted in putaminal coverage of 21% (cohort 1), 34% (cohort 2), and 42% (cohort 3). Cohorts 1, 2, and 3 showed corresponding increases in enzyme activity assessed by PET of 13%, 56%, and 79%, and reductions in antiparkinsonian medication of -15%, -33%, and -42%, respectively, at 6 months. At 12 months, there were dose-related improvements in clinical outcomes, including increases in patient-reported ON-time without troublesome dyskinesia (1.6, 3.3, and 1.5 hours, respectively) and quality of life.InterpretationNovel intraoperative monitoring of administration facilitated targeted delivery of VY-AADC01 in this phase 1 study, which was well tolerated. Increases in enzyme expression and clinical improvements were dose dependent. ClinicalTrials.gov Identifier: NCT01973543 Ann Neurol 2019;85:704-714

QContext: Context-Aware Decomposition for Quantum Gates

In this paper we propose QContext, a new compiler structure that incorporates context-aware and topology-aware decompositions. Because of circuit equivalence rules and resynthesis, variants of a gate-decomposition template may exist. QContext exploits the circuit information and the hardware topology to select the gate variant that increases circuit optimization opportunities. We study the basis-gate-level context-aware decomposition for Toffoli gates and the native-gate-level context-aware decomposition for CNOT gates. Our experiments show that QContext reduces the number of gates as compared with the state-of-the-art approach, Orchestrated Trios.Comment: 10 page