Extensive mass spectrometry-based analysis of the fission yeast proteome: the Schizosaccharomyces pombe PeptideAtlas

We report a high quality and system-wide proteome catalogue covering 71% (3,542 proteins) of the predicted genes of fission yeast, Schizosaccharomyces pombe, presenting the largest protein dataset to date for this important model organism. We obtained this high proteome and peptide (11.4 peptides/protein) coverage by a combination of extensive sample fractionation, high resolution Orbitrap mass spectrometry, and combined database searching using the iProphet software as part of the Trans-Proteomics Pipeline. All raw and processed data are made accessible in the S. pombe PeptideAtlas. The identified proteins showed no biases in functional properties and allowed global estimation of protein abundances. The high coverage of the PeptideAtlas allowed correlation with transcriptomic data in a system-wide manner indicating that post-transcriptional processes control the levels of at least half of all identified proteins. Interestingly, the correlation was not equally tight for all functional categories ranging from r(s) >0.80 for proteins involved in translation to r(s) <0.45 for signal transduction proteins. Moreover, many proteins involved in DNA damage repair could not be detected in the PeptideAtlas despite their high mRNA levels, strengthening the translation-on-demand hypothesis for members of this protein class. In summary, the extensive and publicly available S. pombe PeptideAtlas together with the generated proteotypic peptide spectral library will be a useful resource for future targeted, in-depth, and quantitative proteomic studies on this microorganism

Algorithms for randomness in the behavioral sciences: A tutorial

Simulations and experiments frequently demand the generation of random numbera that have specific distributions. This article describes which distributions should be used for the most cammon problems and gives algorithms to generate the numbers.It is also shown that a commonly used permutation algorithm (Nilsson, 1978) is deficient

DEMAND FOR HERBICIDE IN CORN: AN ENTROPY APPROACH USING MICRO-LEVEL DATA

Price responsiveness of herbicide demand in corn for farmers in Indiana'Â’s White River Basin using cross-section data from individual farms is estimated. Particular attention is paid to appropriate treatment of binding nonnegativity constraints. Estimation was first attempted using an approach to demand systems estimation suggested by Lee and Pitt. However, analytical and computational difficulties effectively preclude estimation by the Lee and Pitt approach. As an alternative, a maximum entropy (ME) approach is presented and discussed. Results from the ME estimator tentatively indicate limited response of herbicide demand to changes in own prices. The maximum entropy approach to demand systems estimation appears to have merit and warrants further attention.Crop Production/Industries, Demand and Price Analysis,

STOCHASTIC TRAFFIC DEMAND PROFILE: INTERDAY VARIATION FOR GIVEN TIME AND DAY OF WEEK

Traffic surveys routinely estimate the profile of traffic demand in a certain road section, showing the expected evolution of the demand over a workday or weekend. However, the actual demand fluctuates around this value. That can lead to brief excess of the capacity at the moment of high demand and consequent congestion due to the capacity drop. This type of traffic demand variability has not yet been properly studied despite the fact it can play significant role in traffic modelling and engineering applications. This paper presents results of analysis of demand variability in five-minute aggregation intervals. The results do not clearly show a single random distribution that would accurately model the demand variability. Normal, lognormal and gamma distributions all show reasonably well fit to the data for individual intervals. Based on count of best fits, the lognormal distribution seems best, but in most cases, the difference between the distributions is not statistically significant. There appears to be a pattern where certain distributions have better fit in different times of day and week. The regularity and magnitude of demand (e.g. morning peak hour) probably play a role in this, as well as the aggregation interval

The engineering design integration (EDIN) system

A digital computer program complex for the evaluation of aerospace vehicle preliminary designs is described. The system consists of a Univac 1100 series computer and peripherals using the Exec 8 operating system, a set of demand access terminals of the alphanumeric and graphics types, and a library of independent computer programs. Modification of the partial run streams, data base maintenance and construction, and control of program sequencing are provided by a data manipulation program called the DLG processor. The executive control of library program execution is performed by the Univac Exec 8 operating system through a user established run stream. A combination of demand and batch operations is employed in the evaluation of preliminary designs. Applications accomplished with the EDIN system are described

Modelling network travel time reliability under stochastic demand

A technique is proposed for estimating the probability distribution of total network travel time, in the light of normal day-to-day variations in the travel demand matrix over a road traffic network. A solution method is proposed, based on a single run of a standard traffic assignment model, which operates in two stages. In stage one, moments of the total travel time distribution are computed by an analytic method, based on the multivariate moments of the link flow vector. In stage two, a flexible family of density functions is fitted to these moments. It is discussed how the resulting distribution may in practice be used to characterise unreliability. Illustrative numerical tests are reported on a simple network, where the method is seen to provide a means for identifying sensitive or vulnerable links, and for examining the impact on network reliability of changes to link capacities. Computational considerations for large networks, and directions for further research, are discussed

Short-term load forecasting based on a semi-parametric additive model

Short-term load forecasting is an essential instrument in power system planning, operation and control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis, and maintenance planning for the generators. Overestimation of electricity demand will cause a conservative operation, which leads to the start-up of too many units or excessive energy purchase, thereby supplying an unnecessary level of reserve. On the contrary, underestimation may result in a risky operation, with insufficient preparation of spinning reserve, causing the system to operate in a vulnerable region to the disturbance. In this paper, semi-parametric additive models are proposed to estimate the relationships between demand and the driver variables. Specifically, the inputs for these models are calendar variables, lagged actual demand observations and historical and forecast temperature traces for one or more sites in the target power system. In addition to point forecasts, prediction intervals are also estimated using a modified bootstrap method suitable for the complex seasonality seen in electricity demand data. The proposed methodology has been used to forecast the half-hourly electricity demand for up to seven days ahead for power systems in the Australian National Electricity Market. The performance of the methodology is validated via out-of-sample experiments with real data from the power system, as well as through on-site implementation by the system operator.Short-term load forecasting, additive model, time series, forecast distribution

Performance of autonomous vehicles in mixed traffic under different demand conditions

Autonomous Vehicles (AVs) are considered one of the potential solutions to future urban mobility with several promised benefits regarding safety and traffic operation. Despite of expected benefits, these vehicles will take decades to have full market penetration and before that, AVs will co-exist with Conventional Vehicles (CVs), which may affect the performance of AVs owing to different driving logic than CVs. The aim of this study is to quantify the impacts of varying penetrations of AVs when introduced in mixed traffic conditions. The study employed simulation environment VISSIM to study the different scenarios based on the percentage of AVs in mixed traffic, category of AVs and varying demand levels. The findings show that at lower demand levels (1000 veh/hr and 2000 veh/hr), CVs and three categories of AVs produced similar results. However, cautious and normal AVs negatively affect traffic operations when the demand level is increased. At demand-3 (3000 veh/hr), the penetration rates of cautious AVs greater than 50% shows negative impact on performance. At demand-4 (4000 veh/hr), even a small proportion (25%) of cautious AVs can negatively affect performance, and a similar effect is observed for normal AVs with a penetration rate greater than 75%. For speed, the minimum reduction with the increase in demand is observed for aggressive AVs, followed by conventional vehicles, normal AVs and cautious AVs. It can be concluded that the aggressive AVs produced better delays, queue length, speed and conflicts than CVs, cautious AVs and normal AVs at the highest demand levels