PepPre: Promote Peptide Identification Using Accurate and Comprehensive Precursors.

Accurate and comprehensive peptide precursor ions are crucial to tandem mass-spectrometry-based peptide identification. An identification engine can derive great advantages from the search space reduction enabled by credible and detailed precursors. Furthermore, by considering multiple precursors per spectrum, both the number of identifications and the spectrum explainability can be substantially improved. Here, we introduce PepPre, which detects precursors by decomposing peaks into multiple isotope clusters using linear programming methods. The detected precursors are scored and ranked, and the high-scoring ones are used for subsequent peptide identification. PepPre is evaluated both on regular and cross-linked peptide data sets and compared with 11 methods. The experimental results show that PepPre achieves a remarkable increase of 203% in PSM and 68% in peptide identifications compared to instrument software for regular peptides and 99% in PSM and 27% in peptide pair identifications for cross-linked peptides, surpassing the performance of all other evaluated methods. In addition to the increased identification numbers, further credibility evaluations evidence the reliability of the identified results. Moreover, by widening the isolation window of data acquisition from 2 to 8 Th, with PepPre, an engine is able to identify at least 64% more PSMs, thereby demonstrating the potential advantages of wide-window data acquisition. PepPre is open-source and available at http://peppre.ctarn.io.

Replacement of Milk and Dairy Products with Soy-Based Alternatives-How to Avoid Nutrient Deficiencies in a Milk-Free Diet?

BACKGROUND: In Germany, milk and dairy products are increasingly replaced by their plant-based alternatives. Although they can be used as substitutes, they differ significantly in their nutrient composition; thus, substitution could lead to nutrient deficiencies. So far, there are no food-based dietary recommendations that show which foods can replace milk and dairy products in a healthy way when switching to a plant-based substitute. OBJECTIVES: Against this background, the question arises as to how to ensure adequate intake of critical nutrients when plant-based alternatives are consumed instead of milk and dairy products. To answer this question, this study aims to analyze what dietary changes would be required to avoid possible nutrient deficiencies and what types of foods can be consumed instead. METHODS: To answer the research question, 3 different models are compared using the linear programming method: healthy diets with 1) milk and dairy products, 2) nonfortified plant-based alternatives, and 3) fortified plant-based alternatives. The models are applied to omnivorous, pescatarian, and vegetarian diet types. RESULTS: The results show that when consuming nonfortified soy alternatives, an adequate supply of calcium and, in the case of a vegetarian diet, vitamin B12 can only be achieved if significant dietary changes are made compared to the average diet. This includes a significantly higher consumption of fruit and vegetables, whereby within the groups, calcium-rich varieties should be chosen (e.g., green leafy vegetables). When consuming fortified soy-based alternatives instead, the absence of milk and dairy products can be well compensated by the nutrients currently added to commercially available products. CONCLUSIONS: Given the trend to consume less milk and dairy products or to abstain from them altogether, public health measures should point out possible nutrient deficiencies as well as necessary dietary changes, especially because in Germany, many plant-based alternatives are not fortified.

Managing Forests for Biodiversity Conservation and Climate Change Mitigation.

We include biodiversity impacts in forest management decision making by incorporating the countryside species area relationship model into the partial equilibrium model GLOBIOM-Forest. We tested three forest management intensities (low, medium, and high) and limited biodiversity loss via an additional constraint on regional species loss. We analyzed two scenarios for climate change mitigation. RCP1.9, the higher mitigation scenario, has more biodiversity loss than the reference RCP7.0, suggesting a trade-off between climate change mitigation, with increased bioenergy use, and biodiversity conservation in forests. This trade-off can be alleviated with biodiversity-conscious forest management by (1) shifting biomass production destined to bioenergy from forests to energy crops, (2) increasing areas under unmanaged secondary forest, (3) reducing forest management intensity, and (4) reallocating biomass production between and within regions. With these mechanisms, it is possible to reduce potential global biodiversity loss by 10% with minor changes in economic outcomes. The global aggregated reduction in biodiversity impacts does not imply that biodiversity impacts are reduced in each ecoregion. We exemplify how to connect an ecologic and an economic model to identify trade-offs, challenges, and possibilities for improved decisions. We acknowledge the limitations of this approach, especially of measuring and projecting biodiversity loss.

Minimizing Computation and Communication Costs of Two-Sided Secure Distributed Matrix Multiplication under Arbitrary Collusion Pattern.

This paper studies the problem of minimizing the total cost, including computation cost and communication cost, in the system of two-sided secure distributed matrix multiplication (SDMM) under an arbitrary collusion pattern. In order to perform SDMM, the two input matrices are split into some blocks, blocks of random matrices are appended to protect the security of the two input matrices, and encoded copies of the blocks are distributed to all computing nodes for matrix multiplication calculation. Our aim is to minimize the total cost, overall matrix splitting factors, number of appended random matrices, and distribution vector, while satisfying the security constraint of the two input matrices, the decodability constraint of the desired result of the multiplication, the storage capacity of the computing nodes, and the delay constraint. First, a strategy of appending zeros to the input matrices is proposed to overcome the divisibility problem of matrix splitting. Next, the optimization problem is divided into two subproblems with the aid of alternating optimization (AO), where a feasible solution can be obtained. In addition, some necessary conditions for the problem to be feasible are provided. Simulation results demonstrate the superiority of our proposed scheme compared to the scheme without appending zeros and the scheme with no alternating optimization.

A nutrition algorithm to optimize feed and medium composition using genome-scale metabolic models.

The optimization of animal feeds and cell culture media are problems of interest to a wide range of industries and scientific disciplines. Both problems are dictated by the properties of an organism's metabolism. However, due to the tremendous complexity of metabolic systems, it can be difficult to predict how metabolism will respond to changes in nutrient availability. A common tool used to capture the complexity of metabolism in a computational framework is a genome-scale metabolic model (GEM). GEMs are useful for predicting the fluxes of reactions within an organism's metabolism. To optimize feed or media, in silico experiments can be performed with GEMs by systematically varying nutritional constraints and predicting metabolic activity. In this way, the influence of various nutritional changes on metabolic outcomes can be evaluated. However, this methodology does not guarantee an optimal solution. Here, we develop a nutrition algorithm that utilizes linear programming to search the entire flux solution space of possible dietary intervention strategies to identify the most efficient changes to nutrition for a desirable metabolic outcome. We illustrate the utility of the nutrition algorithm on GEMs of Atlantic salmon (Salmo salar) and Chinese hamster ovary (CHO) cell metabolism and find that the nutrition algorithm makes predictions that not only align with experimental findings but reveal new insights into promising feeding strategies. We show that the nutrition algorithm is highly versatile and customizable to meet the user's needs. For instance, we demonstrate that the nutrition algorithm can be used to predict feed/media compositions that maximize profit margins. While the nutrition algorithm can be used to define an optimal feed/medium ab initio, it can also identify minimal changes to be made to an existing feed/medium to drive the largest metabolic shift. Moreover, the nutrition algorithm can target multiple metabolic pathways simultaneously with only a marginal increase in computational expense. While the nutrition algorithm has its limitations, we believe that this tool can be leveraged in a broad range of biotechnological applications to enhance the feed/medium optimization process.

Iron Bioavailability Should be Considered when Modeling Omnivorous, Vegetarian, and Vegan Diets.

BACKGROUND: To lower environmental impact of human food consumption, replacement of animal proteins with plant-based proteins is encouraged. However, the lower iron bioavailability of plant-based foods is rarely considered when designing healthy and sustainable diets by using diet modeling. The estimated absorbable iron content of vegetarian and vegan menu plans might therefore be too optimistic. OBJECTIVE: The main aim of this study was to investigate and compare the impact of various methods to estimate absorbable iron intake on the nutritional adequacy of omnivorous, vegetarian, and vegan menu plans designed for women of reproductive age. METHODS: A diet model was developed to design menu plans consisting of a selection of meals that best complied with nutritional requirements. Meals used for modeling were created based on food intake data from the National Health and Nutrition Examination Survey (NHANES). For each meal, absorbable iron concentrations were estimated by using 2 constant absorption factors (18% and 10%) and 2 diet-dependent absorption equations (Conway and Hallberg). For each absorption method and diet type, we used the diet model to design the optimal menu plan. Retrospectively, menu plans were evaluated by estimating the absorbable iron content by using the other absorption methods. RESULTS: Retrospective diet-dependent absorbable iron estimates were consistently lower than estimates based on constant absorption factors. Using diet-dependent estimates increased absorbable iron by optimizing enhancer and inhibitor concentrations. CONCLUSION: Iron bioavailability should be considered when modeling diets.

The potential contribution of house crickets to the dietary zinc content and nutrient adequacy in young Kenyan children: a linear programming analysis using Optifood.

Zn deficiency arising from inadequate dietary intake of bioavailable Zn is common in children in developing countries. Because house crickets are a rich source of Zn, their consumption could be an effective public health measure to combat Zn deficiency. This study used Optifood, a tool based on linear programming analysis, to develop food-based dietary recommendations (FBR) and predict whether dietary house crickets can improve both Zn and overall nutrient adequacy of children's diets. Two quantitative, multi-pass 24-h recalls from forty-seven children aged 2 and 3 years residing in rural Kenya were collected and used to derive model parameters, including a list of commonly consumed foods, median serving sizes and frequency of consumption. Two scenarios were modelled: (i) FBR based on local available foods and (ii) FBR based on local available foods with house crickets. Results revealed that Zn would cease to be a problem nutrient when including house crickets to children's diets (population reference intake coverage for Zn increased from 89 % to 121 % in the best-case scenario). FBR based on both scenarios could ensure nutrient adequacy for all nutrients except for fat, but energy percentage (E%) for fat was higher when house crickets were included in the diet (23 E% v. 19 E%). This manoeuvre, combined with realistic changes in dietary practices, could therefore improve dietary Zn content and ensure adequacy for twelve nutrients for Kenyan children. Further research is needed to render these theoretical recommendations, practical.

Optimising rabies vaccination of dogs in India.

Dog vaccination is the key to controlling rabies in human populations. However, in countries like India, with large free-roaming dog populations, vaccination strategies that rely only on parenteral vaccines are unlikely to be either feasible or successful. Oral rabies vaccines could be used to reach these dogs. We use cost estimates for an Indian city and linear optimisation to find the most cost-effective vaccination strategies. We show that an oral bait handout method for dogs that are never confined can reduce the per dog costs of vaccination and increase vaccine coverage. This finding holds even when baits cost up to 10x the price of parenteral vaccines, if there is a large dog population or proportion of dogs that are never confined. We suggest that oral rabies vaccine baits will be part of the most cost-effective strategies to eliminate human deaths from dog-mediated rabies by 2030.

Supporting the revision of the health benefits package in Uganda: A constrained optimisation approach.

This study demonstrates how the linear constrained optimization approach can be used to design a health benefits package (HBP) which maximises the net disability adjusted life years (DALYs) averted given the health system constraints faced by a country, and how the approach can help assess the marginal value of relaxing health system constraints. In the analysis performed for Uganda, 45 interventions were included in the HBP in the base scenario, resulting in a total of 26.7 million net DALYs averted. When task shifting of pharmacists' and nutrition officers' tasks to nurses is allowed, 73 interventions were included in the HBP resulting in a total of 32 million net DALYs averted (a 20% increase). Further, investing only $58 towards hiring additional nutrition officers' time could avert one net DALY; this increased to $60 and $64 for pharmacists and nurses respectively, and $100,000 for expanding the consumable budget, since human resources present the main constraint to the system.

Platelet inventory management with demand and supply uncertainty and variable pricing considerations.

Managing the inventory of blood is a crucial operation in hospitals owing to its significance in medical treatment. At the same time, blood is characterized by unique attributes, such as perishability and the unpredictable nature of its supply and demand. While models have been developed to optimize the said process, gaps in literature exist in terms of considering the possibility of variable pricing and extensively accounting for uncertainties in the supply chain. In this light, the present study proposes a stochastic multi-period mixed integer linear programming cost minimization model that determines the optimal inventory plan for a hospital purchasing platelets, assuming that prices fluctuate along with the blood center's supply. To implement uncertain supply and demand, the model considers a discrete set of scenarios for each parameter. A study was performed, and the results indicate a promising direction as inventory costs decreased relative to models without the new considerations.

Health information exchange network under collaboration, cooperation, and competition: A game-theoretic approach.

Health Information Exchange (HIE) network allows securely accessing and sharing healthcare-related information among healthcare providers (HCPs) and payers. HIE services are provided by a non-profit/profit organizations under several subscription plans options. A few studies have addressed the sustainability of the HIE network such that HIE providers, HCPs, and payers remain profitable in the long term. However, none of these studies addressed the coexistence of multiple HIE providers in the network. Such coexistence may have a huge impact on the behavior of healthcare systems in terms of adoption rate and HIE pricing strategies. In addition, in spite of all the effort to maintain cooperation between HIE providers, there is still a chance of competition among them in the market. Possible competition among service providers leads to many concerns about the HIE network sustainability and behavior. In this study, a game-theoretic approach to model the HIE market is proposed. Game-theory is used to simulate the behavior of the three different HIE network agents in the HIE market: HIE providers, HCPs, and payers. Pricing strategies and adoption decisions are optimized using a Linear Programming (LP) mathematical model. Results show that the relation between HIEs in the market is crucial to HCP/Payer adoption decision specially to small HCPs. A small change in the discount rate proposed by a competitive HIE provider will highly affect the decision of HCP/payers to join the HIE network. Finally, competition opened the opportunity for more HCPs to join the network due to reduced pricing. Furthermore, collaborative HIEs provided better performance compared to cooperative in terms of profit and HCP adoption rate by sharing their overall costs and revenues.

Rethinking LEO Mega-Constellation Routing to Provide Fast Internet Access Services.

In the realm of providing space-based internet access services, utilizing large-scale low Earth orbit (LEO) satellite networks have emerged as a promising solution for bridging the digital divide and connecting previously unconnected regions. The deployment of LEO satellites can augment terrestrial networks, with increased efficiency and reduced costs. However, as the size of LEO constellations continues to grow, the routing algorithm design of such networks faces numerous challenges. In this study, we present a novel routing algorithm, designated as Internet Fast Access Routing (IFAR), aimed at facilitating faster internet access for users. The algorithm consists of two main components. Firstly, we develop a formal model that calculates the minimum number of hops between any two satellites in the Walker-Delta constellation, along with the corresponding forwarding direction from source to destination. Then, a linear programming is formulated, to match each satellite to the visible satellite on the ground. Upon receipt of user data, each satellite then forwards the data only to the set of visible satellites that correspond to its own satellite. To validate the efficacy of IFAR, we conduct extensive simulation work, and the experimental results showcase the potential of IFAR to enhance the routing capabilities of LEO satellite networks and improve the overall quality of space-based internet access services.

Restoration of linear disturbances from oil-and-gas exploration in boreal landscapes: How can network models help?

In western Canada, decades of oil-and-gas exploration have fragmented boreal landscapes with a dense network of linear forest disturbances (seismic lines). These seismic lines are implicated in the decline in wildlife populations that are adapted to function in unfragmented forest landscapes. In particular, anthropogenic disturbances have led to a decline of woodland caribou populations due to increasing predator access to core caribou habitat. Restoration of seismic lines aims to reduce the landscape fragmentation and stop the decline of caribou populations. However, planning restoration in complex landscapes can be challenging because it must account for a multitude of diverse aspects. To assist with restoration planning, we present a spatial network optimization approach that selects restoration locations in a fragmented landscape while addressing key environmental and logistical constraints. We applied the model to develop restoration scenarios in the Redrock-Prairie Creek caribou range in northwestern Alberta, Canada, which includes a combination of caribou habitat and active oil-and-gas and timber extraction areas. Our study applies network optimization at two distinct scales to address both the broad-scale restoration policy planning and project-level constraints at the level of individual forest sites. We first delineated a contiguous set of coarse-scale regions where restoration is most cost-effective and used this solution to solve a fine-scale network optimization model that addresses environmental and logistical planning constraints at the level of forest patches. Our two-tiered approach helps address the challenges of fine-scale spatial optimization of restoration activities. An additional coarse-scale optimization step finds a feasible starting solution for the fine-scale restoration problem, which serves to reduce the time to find an optimal solution. The added coarse-scale spatial constraints also make the fine-scale restoration solution align with the coarse-scale landscape features, which helps address the broad-scale restoration policies. The approach is generalizable and applicable to assist restoration planning in other regions fragmented by oil-and-gas activities.

A sustainable-circular citrus closed-loop supply chain configuration: Pareto-based algorithms.

Configuration of sustainable supply chains for agricultural products has been a well-known research field recently which is continuing to evolve and grow. It is a complex network design problem, and despite the abundant literature in the field, there are still few models offered to integrate social impacts and environmental effects to support network design decision-making to support the configuration of the citrus supply chain. In this work, the citrus supply chain design problem is investigated by integrating the production, distribution, inventory control, recycling and locational decisions in which the triple bottom lines of sustainability, as well as circularity strategy, are addressed. Accordingly, a novel multi-objective Mixed-Integer Linear Programming (MILP) model is proposed to formulate a multi-period multi-echelon problem to design the sustainable citrus Closed-Loop Supply Chain (CLSC) network. To solve the developed model, the ε-constraint approach is employed in small-sized problems. Furthermore, Strength Pareto Evolutionary Algorithm II (SPEA-II) and Pareto Envelope-based Selection Algorithm II (PESA-II) algorithms are used in medium- and large-sized problems. Taguchi design technique is then utilized to adjust the parameters of the algorithms efficiently. Three well-known assessment metrics and convergence analysis are regarded to test the efficiency of the suggested algorithms. The numerical results demonstrate that the SPEA-II algorithm has a superior efficiency over PESA-II. Moreover, to validate the applicability of the developed methodology, a real case study in Mazandaran/Iran is investigated with the help of a set of sensitivity analyses.

Cost minimizing planning of container inspection and repair in multiple facilities.

A problem of optimal mid-term or long-term planning of inspection and repair of freight containers in multiple facilities is introduced and investigated. The containers are of different types and quality levels, which define their repair costs and workforce requirements. The objective function includes the total holding, inspection, repair, transportation and rejection costs. We propose a deterministic, time-dependent, integer linear min-cost multi-commodity network-flow formulation. The problem is shown to be polynomially solvable if there is a single facility, a single time period and all the containers are repairable and have to be repaired. It is shown to be NP-hard for three important special cases. The computational results of our experiments on randomly generated instances based on real data show that instances of sizes 3 facilities, 4 container types and up to 9 container quality levels can be solved with CPLEX in 5 minutes on a conventional PC, even for 30 periods, with an optimality gap of less than 3%. This is sufficient for medium-term or weekly planning or for short-term recovery planning. However, there are instances of the same magnitude, but with 360 periods of a considerably longer planning horizon, for which an optimality gap of 28% remained even after 10 hours of CPLEX computation.

An optimization model for planning testing and control strategies to limit the spread of a pandemic - The case of COVID-19.

The global health crisis caused by the coronavirus SARS-CoV-2 has highlighted the importance of efficient disease detection and control strategies for minimizing the number of infections and deaths in the population and halting the spread of the pandemic. Countries have shown different preparedness levels for promptly implementing disease detection strategies, via mass testing and isolation of identified cases, which led to a largely varying impact of the outbreak on the populations and health-care systems. In this paper, we propose a new pandemic resource allocation model for allocating limited disease detection and control resources, in particular testing capacities, in order to limit the spread of a pandemic. The proposed model is a novel epidemiological compartmental model formulated as a non-linear programming model that is suitable to address the inherent non-linearity of an infectious disease progression within the population. A number of novel features are implemented in the model to take into account important disease characteristics, such as asymptomatic infection and the distinct risk levels of infection within different segments of the population. Moreover, a method is proposed to estimate the vulnerability level of the different communities impacted by the pandemic and to explicitly consider equity in the resource allocation problem. The model is validated against real data for a case study of COVID-19 outbreak in France and our results provide various insights on the optimal testing intervention time and level, and the impact of the optimal allocation of testing resources on the spread of the disease among regions. The results confirm the significance of the proposed modeling framework for informing policymakers on the best preparedness strategies against future infectious disease outbreaks.

Safe distancing in the time of COVID-19.

The spread of viruses such as SARS-CoV-2 brought new challenges to our society, including a stronger focus on safety across all businesses. Many countries have imposed a minimum social distance among people in order to ensure their safety. This brings new challenges to many customer-related businesses, such as restaurants, offices, theaters, etc., on how to locate their facilities (tables, seats etc.) under distancing constraints. We propose a parallel between this problem and that of locating wind turbines in an offshore area. The discovery of this parallel allows us to apply Mathematical Optimization algorithms originally designed for wind farms, to produce optimized facility layouts that minimize the overall risk of infection among customers. In this way we can investigate the structure of the safest layouts, with some surprising outcomes. A lesson learned is that, in the safest layouts, the facilities are not equally distanced (as it is typically believed) but tend to concentrate on the border of the available area-a policy that significantly reduces the overall risk of contagion.

Linear programming the Ramsar convention's criterion IV (case study: Shadegan Wetland, West Asia).

Wetland ecosystems provide critical ecological services to both humans and wildlife. However, many wetlands around the world are facing challenges that threaten their ecological integrity and jeopardize their status as Ramsar Convention sites. The Shadegan Wetland, a Ramsar site since 1975, has been on the Montreux Record since 1993 due to changing conditions in the wetland. This study aims to utilize linear programming (LP) techniques to evaluate the status of criterion IV of the Ramsar Convention within the Shadegan Wetland. Using mathematical language and Excel software, we defined criterion IV and developed a linear model. The Lingo software was employed to verify the model by setting constraints for proxy variables (X variables). We selected constraints based on extreme climatic conditions, such as energy and water limitations, during the study period while considering the trend of each variable. By identifying effective interventions for promoting sustainable use of the wetland while preserving its ecological balance, the LP can support the efforts to re-nominate the Shadegan Wetland as a Ramsar site. Considering the critical conditions, the lowest value of Z in the studied period unravels the critical year as the target. Based on the result, 2015 with the lowest value of the Z index (- 0.36) was identified as the critical year in the entire study period starting from 2001-2019. In the critical year itself, the population of birds equals 50,000 birds, while the average population of birds over the course of the past 20 years was nearly 37,000 birds.

Developing an integrated land allocation model based on linear programming and game theory.

Land use configuration in any given landscape is the result of a multi-objective optimization process, which takes into account different ecological, economic, and social factors. In this process, coordinating stakeholders is a key factor to successful spatial land use optimization. Stakeholders need to be modeled as players who have the ability to interact with each other towards their best solution, while considering multiple goals and constraints at the same time. Game theory provides a tool for land use planners to model and analyze such interactions. In order to apply the spatial allocation model and address stakeholder conflicts, an integrated model based on linear programming and game theory was designed in this study. For implementing such model, we conducted an optimal land use allocation process through multi-objective land allocation (MOLA) and linear programming methods. Then, two groups of environmental and land development players were considered to implement the optimization model. The game algorithm was used to select the appropriate constraint so that the result would be acceptable to all stakeholders. The results showed that during the third round of the game, the decision-making process and the optimization of land uses reached the desired Nash Equilibrium state and the conflict between stakeholders was resolved. Ultimately, in order to localize the results, a suitable solution was presented in a GIS environment.

Optimizing the COVID-19 cold chain vaccine distribution network with medical waste management: A robust optimization approach.

This paper investigates the distribution problem of the COVID-19 vaccine at the provincial level in Turkey and the management of medical waste, considering the cold chain requirements and the perishable nature of vaccines. In this context, a novel multi-period multi-objective mixed-integer linear programming model is initially presented over a 12-month planning horizon for solving the deterministic distribution problem. The model includes newly structured constraints due to the feature of COVID-19 vaccines, which must be administered in two doses at specified intervals. Then, the presented model is tested for the province of Izmir with deterministic data, and the results show that the demand can be satisfied and community immunity can be achieved in the specified planning horizon. Moreover, for the first time, a robust model is created using polyhedral uncertainty sets to manage uncertainties related to supply and demand quantities, storage capacity, and deterioration rate, and it has been analyzed under different uncertainty levels. Accordingly, as the level of uncertainty increases, the percentage of meeting the demand gradually decreases. It is observed that the biggest effect here is the uncertainty in supply, and in the worst case, approximately 30% of the demand cannot be met.