Dexamethasone is a glucocorticoid steroid with anti-inflammatory properties used to treat many diseases, including cancer, in which it helps manage various side effects of chemo-, radio-, and ...immunotherapies. Here, we investigate the tumor microenvironment (TME)-normalizing effects of dexamethasone in metastatic murine breast cancer (BC). Dexamethasone normalizes vessels and the extracellular matrix, thereby reducing interstitial fluid pressure, tissue stiffness, and solid stress. In turn, the penetration of 13 and 32 nm dextrans, which represent nanocarriers (NCs), is increased. A mechanistic model of fluid and macromolecule transport in tumors predicts that dexamethasone increases NC penetration by increasing interstitial hydraulic conductivity without significantly reducing the effective pore diameter of the vessel wall. Also, dexamethasone increases the tumor accumulation and efficacy of ∼30 nm polymeric micelles containing cisplatin (CDDP/m) against murine models of primary BC and spontaneous BC lung metastasis, which also feature a TME with abnormal mechanical properties. These results suggest that pretreatment with dexamethasone before NC administration could increase efficacy against primary tumors and metastases.
Semi-Infinite Optimization with Hybrid Models Wang, Chenyu; Wilhelm, Matthew E; Stuber, Matthew D
Industrial & engineering chemistry research,
04/2022, Letnik:
61, Številka:
15
Journal Article
Recenzirano
The robust design of performance/safety-critical process systems, from a model-based perspective, remains an existing challenge. Hybrid first-principles data-driven models offer the potential to ...dramatically improve model prediction accuracy, stepping closer to the digital twin concept. Within this context, worst-case engineering design feasibility and reliability problems give rise to a class of semi-infinite program (SIP) formulations with hybrid models as coupling equality constraints. Reduced-space deterministic global optimization methods are exploited to solve this class of SIPs to ϵ-global optimality in finitely many iterations. This approach is demonstrated on two challenging case studies: a nitrification reactor for a wastewater treatment system to address worst-case feasibility verification of dynamical systems and a three-phase separation system plagued by numerical domain violations to demonstrate how they can be overcome using a nonsmooth SIP formulation with hybrid models and a validity constraint incorporated.
In this work, we present general methods to construct convex/concave relaxations of the activation functions that are commonly chosen for artificial neural networks (ANNs). The choice of these ...functions is often informed by both broader modeling considerations balanced with a need for high computational performance. The direct application of factorable programming techniques to compute bounds and convex/concave relaxations of such functions often lead to weak enclosures due to the dependency problem. Moreover, the piecewise formulation that defines several popular activation functions, prevents the computation of convex/concave relaxations as they violate the factorable function requirement. To improve the performance of relaxations of ANNs for deterministic global optimization applications, this study presents the development of a library of envelopes of the thoroughly studied rectifier-type and sigmoid activation functions, in addition to the novel self-gated sigmoid-weighted linear unit (SiLU) and Gaussian error linear unit activation functions. We demonstrate that the envelopes of activation functions directly lead to tighter relaxations of ANNs on their input domain. In turn, these improvements translate to a dramatic reduction in CPU runtime required for solving optimization problems involving ANN models to epsilon-global optimality. We further demonstrate that the factorable programming approach leads to superior computational performance over alternative state-of-the-art approaches.
The Global Land‐Atmosphere Climate Experiment–Coupled Model Intercomparison Project phase 5 (GLACE‐CMIP5) is a multimodel experiment investigating the impact of soil moisture‐climate feedbacks in ...CMIP5 projections. We present here first GLACE‐CMIP5 results based on five Earth System Models, focusing on impacts of projected changes in regional soil moisture dryness (mostly increases) on late 21st century climate. Projected soil moisture changes substantially impact climate in several regions in both boreal and austral summer. Strong and consistent effects are found on temperature, especially for extremes (about 1–1.5 K for mean temperature and 2–2.5 K for extreme daytime temperature). In the Northern Hemisphere, effects on mean and heavy precipitation are also found in most models, but the results are less consistent than for temperature. A direct scaling between soil moisture‐induced changes in evaporative cooling and resulting changes in temperature mean and extremes is found in the simulations. In the Mediterranean region, the projected soil moisture changes affect about 25% of the projected changes in extreme temperature.
Key Points
GLACE‐CMIP5 quantifies soil moisture feedbacks in climate projections
Impacts on late 21st century temperature and precipitation mean and extremes
Effects of about 25% for temperature extremes in Mediterranean region
Deterministic nonconvex optimization solvers generate convex relaxations of nonconvex functions by making use of underlying factorable representations. One approach introduces auxiliary variables ...assigned to each factor that lifts the problem into a higher-dimensional decision space. In contrast, a generalized McCormick relaxation approach offers the significant advantage of constructing relaxations in the lower dimensionality space of the original problem without introducing auxiliary variables, often referred to as a “reduced-space” approach. Recent contributions illustrated how additional nontrivial inequality constraints may be used in factorable programming to tighten relaxations of the ubiquitous bilinear term. In this work, we exploit an analogous representation of McCormick relaxations and factorable programming to formulate tighter relaxations in the original decision space. We develop the underlying theory to generate necessarily tighter reduced-space McCormick relaxations when a priori convex/concave relaxations are known for intermediate bilinear terms. We then show how these rules can be generalized within a McCormick relaxation scheme via three different approaches: the use of a McCormick relaxations coupled to affine arithmetic, the propagation of affine relaxations implied by subgradients, and an enumerative approach that directly uses relaxations of each factor. The developed approaches are benchmarked on a library of optimization problems using the EAGO.jl optimizer. Two case studies are also considered to demonstrate the developments: an application in advanced manufacturing to optimize supply chain quality metrics and a global dynamic optimization application for rigorous model validation of a kinetic mechanism. The presented subgradient method leads to an improvement in CPU time required to solve the considered problems to
ϵ
-global optimality.
We present a deterministic global optimization method for nonlinear programming formulations constrained by stiff systems of ordinary differential equation (ODE) initial value problems (IVPs). The ...examples arise from dynamic optimization problems exhibiting both fast and slow transient phenomena commonly encountered in model‐based systems engineering applications. The proposed approach utilizes unconditionally stable implicit integration methods to reformulate the ODE‐constrained problem into a nonconvex nonlinear program (NLP) with implicit functions embedded. This problem is then solved to global optimality in finite time using a spatial branch‐and‐bound framework utilizing convex/concave relaxations of implicit functions constructed by a method which fully exploits problem sparsity. The algorithms were implemented in the Julia programming language within the EAGO.jl package and demonstrated on five illustrative examples with varying complexity relevant in process systems engineering. The developed methods enable the guaranteed global solution of dynamic optimization problems with stiff ODE–IVPs embedded.
•Method to maximize FDI effectiveness at the worst-case realization of uncertainty.•Fault diagnostic test guarantees system safety over the entire domain of uncertainty.•FDI problem formulated as ...semi-infinite program with implicit functions embedded.•FDI prerformance benefits illustrated through k-NN classification.
The increasing uncertainty due to advances in modern systems has negatively impacted system health and safety. System health and safety problems associated with uncertainty can be attributed to (1) inadequate system design, (2) ill-suited controller and operating envelope, and (3) lack of robustness in system diagnostics. The latter issue (3) is the main focus of this paper. This work presents an algorithm for the design of active fault detection and isolation (FDI) tests that provide rigorous guarantees of robustness in safety-critical systems. A semi-infinite program with implicit functions embedded is formulated with the objective of maximizing FDI effectiveness at the worst-case realization of uncertainty by manipulating admissible system inputs, while taking into account system safety constraints. This problem is solved locally and globally illustrating deficiencies in the performance of FDI tests designed for the mean values of anticipated uncertainty. The resulting solution is an optimally performing fault diagnostic test that guarantees system safety over the entire domain of uncertainty. This is illustrated using a benchmark three-tank system and further analyzed through Monte Carlo simulation and k-NN classification.
The ancestor of termites relied on gut symbionts for degradation of plant material, an association that persists in all termite families.1,2 However, the single-lineage Macrotermitinae has ...additionally acquired a fungal symbiont that complements digestion of food outside the termite gut.3 Phylogenetic analysis has shown that fungi grown by these termites form a clade—the genus Termitomyces—but the events leading toward domestication remain unclear.4 To address this, we reconstructed the lifestyle of the common ancestor of Termitomyces using a combination of ecological data with a phylogenomic analysis of 21 related non-domesticated species and 25 species of Termitomyces. We show that the closely related genera Blastosporella and Arthromyces also contain insect-associated species. Furthermore, the genus Arthromyces produces asexual spores on the mycelium, which may facilitate insect dispersal when growing on aggregated subterranean fecal pellets of a plant-feeding insect. The sister-group relationship between Arthromyces and Termitomyces implies that insect association and asexual sporulation, present in both genera, preceded the domestication of Termitomyces and did not follow domestication as has been proposed previously. Specialization of the common ancestor of these two genera on an insect-fecal substrate is further supported by similar carbohydrate-degrading profiles between Arthromyces and Termitomyces. We describe a set of traits that may have predisposed the ancestor of Termitomyces toward domestication, with each trait found scattered in related taxa outside of the termite-domesticated clade. This pattern indicates that the origin of the termite-fungus symbiosis may not have required large-scale changes of the fungal partner.
•Insect-fecal associations predate the domestication of Termitomyces fungi•A set of morphological traits predisposed lyophylloid fungi toward domestication•Insect-associated lyophylloid fungi have reduced plant-degrading capabilities•This symbiosis may have been facilitated by pre-adaptation of both partners
How termites came to domesticate Termitomyces fungi is unknown. van de Peppel et al. identify a set of ecological, morphological, and genomic traits shared by domesticated Termitomyces and the insect-associated sister group Arthromyces. These may have served as the basis for domestication.
Abstract To mimic the extracellular matrix surrounding high grade gliomas, composite matrices composed of either acid-solubilized (AS) or pepsin-treated (PT) collagen and the glycosaminoglycans ...chondroitin sulfate (CS) and hyaluronic acid (HA) are prepared and characterized. The structure and mechanical properties of collagen/CS and collagen/HA gels are studied via confocal reflectance microscopy (CRM) and rheology. CRM reveals that CS induces fibril bundling and increased mesh size in AS collagen but not PT collagen networks. The presence of CS also induces more substantial changes in the storage and loss moduli of AS gels than of PT gels, in accordance with expectation based on network structural parameters. The presence of HA significantly reduces mesh size in AS collagen but has a smaller effect on PT collagen networks. However, both AS and PT collagen network viscoelasticity is strongly affected by the presence of HA. The effects of CS and HA on glioma invasion is then studied in collagen/GAG matrices with network structure both similar to (PT collagen-based gels) and disparate from (AS collagen-based gels) those of the corresponding pure collagen matrices. It is shown that CS inhibits and HA has no significant effect on glioma invasion in 1.0 mg/ml collagen matrices over 3 days. The inhibitory effect of CS on glioma invasion is more apparent in AS than in PT collagen gels, suggesting invasive behavior in these environments is affected by both biochemical and network morphological changes induced by GAGs. This study is among the few efforts to differentiate structural, mechanical and biochemical effects of changes to matrix composition on cell motility in 3D.
Honey bee queens are exceptionally promiscuous. Early in life, queens perform one to five nuptial flights, mating with up to 44 drones. Many studies have documented potential benefits of multiple ...mating. In contrast, potential costs of polyandry and the sensitivity of queens to such costs have largely been ignored because they are difficult to address experimentally. To consider one aspect of mating costs to queens, the difficulty of flight, we compared flight behavior and success among a group of control queens and two experimental groups of queens that carried lead weights of two different sizes. For each queen, we assessed the number and duration of all flights and, after egg-laying commenced, the amount of stored sperm and the number of mates in terms of the offspring's patrilineal genetic diversity. Added weights quantitatively decreased the number of flights, the mean duration of flights and consequently the total time spent flying. Mating success in terms of sperm quantity and patrilines detected among the queens' offspring was also negatively impacted by the experimental manipulation. Thus, it can be concluded that the flight effort of honey bee queens during their mating period is adjusted in response to an experimentally increased cost of flying with multiple consequences for their mating success. Our results suggest that queen behavior is flexible and mating costs deserve more attention to explain the extreme polyandry in honey bees.
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