182 research outputs found
Decision-Making and the Newsvendor Problem – An Experimental Study
This paper investigates repetitive purchase decisions of perishable items in the face of uncertain demand (the newsvendor problem). The experimental design includes: high, or low profit levels; and uniform, or normal demand distributions. The results show that in all cases both learning and convergence occur and are effected by: (1) the mean demand; (2) the order-size of the maximal expected profit; and (3) the demand level of the immediately preceding round. In all cases of the experimental design, the purchase order converges to a value between the mean demand and the quantity for maximizing the expected profit.Inventory, Learning, Behavior, Management, Optimization
On the Geometric Interpretation of the Nonnegative Rank
The nonnegative rank of a nonnegative matrix is the minimum number of
nonnegative rank-one factors needed to reconstruct it exactly. The problem of
determining this rank and computing the corresponding nonnegative factors is
difficult; however it has many potential applications, e.g., in data mining,
graph theory and computational geometry. In particular, it can be used to
characterize the minimal size of any extended reformulation of a given
combinatorial optimization program. In this paper, we introduce and study a
related quantity, called the restricted nonnegative rank. We show that
computing this quantity is equivalent to a problem in polyhedral combinatorics,
and fully characterize its computational complexity. This in turn sheds new
light on the nonnegative rank problem, and in particular allows us to provide
new improved lower bounds based on its geometric interpretation. We apply these
results to slack matrices and linear Euclidean distance matrices and obtain
counter-examples to two conjectures of Beasly and Laffey, namely we show that
the nonnegative rank of linear Euclidean distance matrices is not necessarily
equal to their dimension, and that the rank of a matrix is not always greater
than the nonnegative rank of its square
Enhanced production yields of rVSV-SARS-CoV-2 vaccine using Fibra-Cel® macrocarriers
The COVID-19 pandemic has led to high global demand for vaccines to safeguard public health. To that end, our institute has developed a recombinant viral vector vaccine utilizing a modified vesicular stomatitis virus (VSV) construct, wherein the G protein of VSV is replaced with the spike protein of SARS-CoV-2 (rVSV-ΔG-spike). Previous studies have demonstrated the production of a VSV-based vaccine in Vero cells adsorbed on Cytodex 1 microcarriers or in suspension. However, the titers were limited by both the carrier surface area and shear forces. Here, we describe the development of a bioprocess for rVSV-ΔG-spike production in serum-free Vero cells using porous Fibra-Cel® macrocarriers in fixed-bed BioBLU®320 5p bioreactors, leading to high-end titers. We identified core factors that significantly improved virus production, such as the kinetics of virus production, the use of macrospargers for oxygen supply, and medium replenishment. Implementing these parameters, among others, in a series of GMP production processes improved the titer yields by at least two orders of magnitude (2e9 PFU/mL) over previously reported values. The developed process was highly effective, repeatable, and robust, creating potent and genetically stable vaccine viruses and introducing new opportunities for application in other viral vaccine platforms
Deciphering the Arginine-Binding Preferences at the Substrate-Binding Groove of Ser/Thr Kinases by Computational Surface Mapping
Protein kinases are key signaling enzymes that catalyze the transfer of γ-phosphate from an ATP molecule to a phospho-accepting residue in the substrate. Unraveling the molecular features that govern the preference of kinases for particular residues flanking the phosphoacceptor is important for understanding kinase specificities toward their substrates and for designing substrate-like peptidic inhibitors. We applied ANCHORSmap, a new fragment-based computational approach for mapping amino acid side chains on protein surfaces, to predict and characterize the preference of kinases toward Arginine binding. We focus on positions P−2 and P−5, commonly occupied by Arginine (Arg) in substrates of basophilic Ser/Thr kinases. The method accurately identified all the P−2/P−5 Arg binding sites previously determined by X-ray crystallography and produced Arg preferences that corresponded to those experimentally found by peptide arrays. The predicted Arg-binding positions and their associated pockets were analyzed in terms of shape, physicochemical properties, amino acid composition, and in-silico mutagenesis, providing structural rationalization for previously unexplained trends in kinase preferences toward Arg moieties. This methodology sheds light on several kinases that were described in the literature as having non-trivial preferences for Arg, and provides some surprising departures from the prevailing views regarding residues that determine kinase specificity toward Arg. In particular, we found that the preference for a P−5 Arg is not necessarily governed by the 170/230 acidic pair, as was previously assumed, but by several different pairs of acidic residues, selected from positions 133, 169, and 230 (PKA numbering). The acidic residue at position 230 serves as a pivotal element in recognizing Arg from both the P−2 and P−5 positions
Large-scale unit commitment under uncertainty: an updated literature survey
The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject
The Cell Tracking Challenge: 10 years of objective benchmarking
The Cell Tracking Challenge is an ongoing benchmarking initiative that
has become a reference in cell segmentation and tracking algorithm
development. Here, we present a signifcant number of improvements
introduced in the challenge since our 2017 report. These include the
creation of a new segmentation-only benchmark, the enrichment of
the dataset repository with new datasets that increase its diversity and
complexity, and the creation of a silver standard reference corpus based
on the most competitive results, which will be of particular interest for
data-hungry deep learning-based strategies. Furthermore, we present
the up-to-date cell segmentation and tracking leaderboards, an in-depth
analysis of the relationship between the performance of the state-of-the-art
methods and the properties of the datasets and annotations, and two
novel, insightful studies about the generalizability and the reusability
of top-performing methods. These studies provide critical practical
conclusions for both developers and users of traditional and machine
learning-based cell segmentation and tracking algorithms.Web of Science2071020101
Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults
Background Underweight and obesity are associated with adverse health outcomes throughout the life course. We
estimated the individual and combined prevalence of underweight or thinness and obesity, and their changes, from
1990 to 2022 for adults and school-aged children and adolescents in 200 countries and territories.
Methods We used data from 3663 population-based studies with 222 million participants that measured height and
weight in representative samples of the general population. We used a Bayesian hierarchical model to estimate
trends in the prevalence of different BMI categories, separately for adults (age ≥20 years) and school-aged children
and adolescents (age 5–19 years), from 1990 to 2022 for 200 countries and territories. For adults, we report the
individual and combined prevalence of underweight (BMI <18·5 kg/m2) and obesity (BMI ≥30 kg/m2). For schoolaged children and adolescents, we report thinness (BMI <2 SD below the median of the WHO growth reference)
and obesity (BMI >2 SD above the median).
Findings From 1990 to 2022, the combined prevalence of underweight and obesity in adults decreased in
11 countries (6%) for women and 17 (9%) for men with a posterior probability of at least 0·80 that the observed
changes were true decreases. The combined prevalence increased in 162 countries (81%) for women and
140 countries (70%) for men with a posterior probability of at least 0·80. In 2022, the combined prevalence of
underweight and obesity was highest in island nations in the Caribbean and Polynesia and Micronesia, and
countries in the Middle East and north Africa. Obesity prevalence was higher than underweight with posterior
probability of at least 0·80 in 177 countries (89%) for women and 145 (73%) for men in 2022, whereas the converse
was true in 16 countries (8%) for women, and 39 (20%) for men. From 1990 to 2022, the combined prevalence of
thinness and obesity decreased among girls in five countries (3%) and among boys in 15 countries (8%) with a
posterior probability of at least 0·80, and increased among girls in 140 countries (70%) and boys in 137 countries (69%)
with a posterior probability of at least 0·80. The countries with highest combined prevalence of thinness and
obesity in school-aged children and adolescents in 2022 were in Polynesia and Micronesia and the Caribbean for
both sexes, and Chile and Qatar for boys. Combined prevalence was also high in some countries in south Asia, such
as India and Pakistan, where thinness remained prevalent despite having declined. In 2022, obesity in school-aged
children and adolescents was more prevalent than thinness with a posterior probability of at least 0·80 among girls
in 133 countries (67%) and boys in 125 countries (63%), whereas the converse was true in 35 countries (18%) and
42 countries (21%), respectively. In almost all countries for both adults and school-aged children and adolescents,
the increases in double burden were driven by increases in obesity, and decreases in double burden by declining
underweight or thinness.
Interpretation The combined burden of underweight and obesity has increased in most countries, driven by an
increase in obesity, while underweight and thinness remain prevalent in south Asia and parts of Africa. A healthy
nutrition transition that enhances access to nutritious foods is needed to address the remaining burden of
underweight while curbing and reversing the increase in obesit
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