No abstract

In December 2019, the Chinese city of Wuhan was the center of origin of a pneumonialike disease outbreak with an unknown causative pathogen. The CDC, China, managed to track the source of infection to a novel coronavirus (2019-nCoV; SARS-CoV-2) that shares approximately 79.6% of its genome with SARS-CoV. The World Health Organization (WHO) initially declared COVID-19 as a Public Health Emergency of International Concern (PHEIC) and later characterized it as a global pandemic on March 11, 2020. Due to the novel nature of this virus, there is an urgent need for vaccines and therapeutics to control the spread of SARS-CoV-2 and its associated disease, COVID-19. Global efforts are underway to circumvent its further spread and treat COVID-19 patients through experimental vaccine formulations and therapeutic interventions, respectively. In the absence of any effective therapeutics, we have devised h bioinformatics-based approaches to accelerate global efforts in the fight against SARS-CoV-2 and to assist researchers in the initial phase of vaccine and therapeutics development. In this study, we have performed comprehensive meta-analyses and developed an integrative resource, "CoronaVR" (http://bioinfo.imtech.res.in/manojk/ coronavr/). Predominantly, we identified potential epitope-based vaccine candidates, siRNA-based therapeutic regimens, and diagnostic primers. The resource is categorized into the main sections "Genomes," "Epitopes," "Therapeutics," and Primers." The genome section harbors different components, viz, genomes, a genome browser, phylogenetic analysis, codon usage, glycosylation sites, and structural analysis. Under the umbrella of epitopes, subdivisions , namely cross-protective epitopes, B-cell (linear/discontinuous), T-cell (CD4 + /CD8 +), CTL, and MHC binders, are presented. The therapeutics section has different subsections like siRNA, miRNAs, and sgRNAs. Further, experimentally confirmed and designed diagnostic primers are earmarked in the primers section. Our study provided a set of shortlisted B-cell and T-cell (CD4 +

In order to reduce the cost of new intercity passenger rail corridors, the operation of higher speed passenger networks on existing freight corridors is being examined and considered. The issues to be addressed in such operations include the one-time upgrade of the track to allow for this higher speed passenger traffic and the ongoing maintenance costs necessary to maintain this track for the mixed higher speed passenger and freight operations. This latter issue is usually addressed in the access agreements for the corridor, and must include how these costs are to be shared. A recent US Federal Railroad Administration study specifically addressed the issue of “steady state” maintenance costs for mixed use corridors consisting on this class of higher speed passenger operations and concurrent freight operations, to include heavy axle load freight operations. The result of that study was a “planner’s handbook” for estimating these track maintenance costs, as part of the overall analysis of the feasibility and cost of operating higher speed passenger traffic on existing freight corridors. This paper presents the methodology used in the development of the methodology for estimating maintenance costs for mixed higher speed passenger and freight rail corridors (Classes 4, 5 and 6). Specifically, it addresses the estimation of these “steady state” infrastructure maintenance costs for a range of operating scenarios with different combination of passenger and freight traffic densities and operating speeds. These infrastructure costs include track, bridge and building (B&B), and communications and signal (C&S) costs. The resulting costs are presented as a set of cost matrices both in terms of a total cost per track mile and in terms of cost per passenger train mile. The cost matrices cover a range of combinations of traffic and track configuration, with minimum and maximum costs developed for each cell in the cost matrices. The minimum costs are based on maintenance standards geared to typical Class I freight railroad practice, such as where passenger trains currently operate on a freight railroad right of way, while the maximum costs reflect maintenance practices on existing high speed railroad track. This paper provides a description of the analytic models used to generate the costs, and the process by which those models were calibrated to actual cost data to develop costs for a wide range of traffic and track combinations. Sample application of the methodology to include several proposed mixed use corridors is also presented.

The purpose of this analysis was to quantify the business benefits of Positive Train Control (PTC) for the Class I freight railroad industry. This report does not address the safety benefits of PTC. These were previously quantified by the Rail Safety Advisory Committee (RSAC), which identified nearly a thousand "PPAs" (PTC-preventable accidents) on U.S. railroads over a 12-year period, and determined the savings to be realized from each avoided accident. The RSAC finding was that avoidance of these PPAs was not, by itself, sufficient (from a strictly economic point of view) to justify an investment in PTC. Examples of potential business benefits include: * Line capacity enhancement * Improved service reliability * Faster over-the-road running times * More efficient use of cars and locomotives (made possible by real-time location information) * Reduction in locomotive failures (due to availability of real-time diagnostics) * Larger "windows" (periods during which no trains operate and maintenance workers can safely occupy the track) for track maintenance (made possible by real-time location information) * Fuel savings This paper presents the results of the analysis. It is important to recognize, however, that the state of the art in making these estimates is not sufficiently mature to make exact answers feasible. Presented here are the best estimates now possible, with observations as to how better information may be developed. Benefits were estimated in the above areas and the cost of deploying PTC on the Class I network (99,000 route miles and 20,000 locomotives) were calculated. The conclusions of the analysis were as follows: * Deployment of PTC on the Class I railroad network (99,000 route miles, 20,000 locomotives) would cost between $2.3 billion and $4.4 billion over five years * Annual benefits, once the system was fully implemented, were estimated at $2.2 billion to $3.8 billion * Internal rate of return was estimated (depending on timing and cost) to be between 44% and 160%