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At least 253 records · Page 14

Physics-based adaptivity of a spectral method for the Vlasov–Poisson equations based on the asymmetrically-weighted Hermite expansion in velocity space

We propose a spectral method for the 1D-1V Vlasov–Poisson system where the discretization in velocity space is based on asymmetrically-weighted Hermite functions, dynamically adapted via a scaling α and shifting u of the velocity variable. Specifically, at each time instant an adaptivity criterion selects new values of α and u based on the numerical solution of the discrete Vlasov–Poisson system obtained at that time step. Once the new values of the Hermite parameters α and u are fixed, the Hermite expansion is updated and the discrete system is further evolved for the next time step. The procedure is applied iteratively over the desired temporal interval. The key aspects of the adaptive algorithm are: the map between approximation spaces associated with different values of the Hermite parameters that preserves total mass, momentum and energy; and the adaptivity criterion to update α and u based on physics considerations relating the Hermite parameters to the average velocity and temperature of each plasma species. For the discretization of the spatial coordinate, we rely on Fourier functions and use the implicit midpoint rule for time stepping. The resulting numerical method possesses intrinsically the property of fluid-kinetic coupling, where the low-order terms of the expansion are akin to the fluid moments of a macroscopic description of the plasma, while kinetic physics is retained by adding more spectral terms. Moreover, the scheme features conservation of total mass, momentum and energy associated in the discrete, for periodic boundary conditions. A set of numerical experiments confirms that the adaptive method outperforms the non-adaptive one in terms of accuracy and stability of the numerical solution.

97 MATHEMATICS AND COMPUTING↗

The contribution of the neuroendocrine system to adaption after repeated daily ozone exposure in rats

Ozone-induced lung injury/inflammation dissipates despite continued exposure for 3 or more days; however, the mechanisms of adaptation/habituation remain unclear. Since ozone effects are mediated through adrenal-derived stress hormones, which also regulate longevity of centrally-mediated stress response, we hypothesized that ozone-adaptation is linked to diminution of neuroendocrine stress-axes activation and glucocorticoid levels. Male Wistar-Kyoto-rats (12-week-old) were injected with vehicle or a therapeutically-relevant dexamethasone dose (0.01-mg/kg/day; intraperitoneal) for 1-month to determine if suppression of glucocorticoid signaling was linked to adaptation. Vehicle- and dexamethasone-treated rats were exposed to air or 0.8-ppm ozone, 4 h/day × 2 or 4 days to assess the impacts of acute exposure and adaptation, respectively. Dexamethasone reduced thymus and spleen weights, circulating lymphocytes, corticosterone and increased insulin. Ozone increased lavage-fluid protein and neutrophils and decreased circulating lymphocytes at day-2 but not day-4. Ozone-induced hyperglycemia, glucose intolerance and inhibition of beta-cell insulin release occurred at day-1 but not day-3. Ozone depleted circulating prolactin, thyroid-stimulating hormone, and luteinizing-hormone at day-2 but not day-4, suggesting central mediation of adaptation. Adrenal epinephrine biosynthesis gene, Pnmt, was up-regulated after ozone exposure at both timepoints. However, genes involved in glucocorticoid biosynthesis were up-regulated after day-2 but not day-4, suggesting that acute 1- or 2-day ozone-mediated glucocorticoid increase elicits feedback inhibition to dampen hypothalamic stimulation of ACTH release in response to repeated subsequent ozone exposures. Although dexamethasone pretreatment affected circulating insulin, lymphocytes and adrenal genes, it had modest effect on ozone adaptation. In conclusion, ozone adaptation likely involves lack of hypothalamic response due to reduced availability of circulating glucocorticoids.

60 APPLIED LIFE SCIENCES↗

Quantitative comparison of adaptive sampling methods for protein dynamics

Adaptive sampling methods, often used in combination with Markov state models, are becoming increasingly popular for speeding up rare events in simulation such as molecular dynamics (MD) without biasing the system dynamics. Several adaptive sampling strategies have been proposed, but it is not clear which methods perform better for different physical systems. Here, we present a systematic evaluation of selected adaptive sampling strategies on a wide selection of fast folding proteins. The adaptive sampling strategies were emulated using models constructed on already existing MD trajectories. We provide theoretical limits for the sampling speed-up and compare the performance of different strategies with and without using some a priori knowledge of the system. The results show that for different goals, different adaptive sampling strategies are optimal. In order to sample slow dynamical processes such as protein folding without a priori knowledge of the system, a strategy based on the identification of a set of metastable regions is consistently the most efficient, while a strategy based on the identification of microstates performs better if the goal is to explore newer regions of the conformational space. Interestingly, the maximum speed-up achievable for the adaptive sampling of slow processes increases for proteins with longer folding times, encouraging the application of these methods for the characterization of slower processes, beyond the fast-folding proteins considered here.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Decentralized Filtering Adaptive Neural Network Control for Uncertain Switched Interconnected Nonlinear Systems

This article presents a novel decentralized filtering adaptive neural network control framework for uncertain switched interconnected nonlinear systems. Each subsystem has its own decentralized controller based on the established decentralized state predictor. For each subsystem, the nonlinear uncertainties are approximated by a Gaussian radial basis function (GRBF) neural network incorporated with a piecewise constant adaptive law, where the adaptive law will update adaptive parameters from the error dynamics between the host system and the decentralized state predictor by discarding the unknowns, whereas a decentralized filtering control law is derived to cancel both local and mismatched uncertainties from other subsystems, as well as achieve the local objective tracking of the host system. The achievement of global objective depends on the achievement of local objective for each subsystem. The matched uncertainties are canceled directly by adopting their opposite in the control signal, whereas a dynamic inversion of the system is required to eliminate the effect of the mismatched uncertainties on the output. By exploiting the average dwell time principle, the error bounds between the real system and the virtual reference system, which defines the best performance that can be achieved by the closed-loop system, are derived. A numerical example is given to illustrate the effectiveness of the decentralized filtering adaptive neural network control architecture by comparing against the model reference adaptive control (MRAC).

Average dwell time, decentralized, filtering adapt↗

AMM: Adaptive Multilinear Meshes

Adaptive representations are increasingly indispensable for reducing the in-memory and on-disk footprints of large-scale data. Usual solutions are designed broadly along two themes: reducing data precision, e.g., through compression, or adapting data resolution, e.g., using spatial hierarchies. Additionally, recent research suggests that combining the two approaches, i.e., adapting both resolution and precision simultaneously, can offer significant gains over using them individually. However, there currently exist no practical solutions to creating and evaluating such representations at scale. In this work, we present a new resolution-precision-adaptive representation to support hybrid data reduction schemes and offer an interface to existing tools and algorithms. Through novelties in spatial hierarchy, our representation, Adaptive Multilinear Meshes (AMM), provides considerable reduction in the mesh size. AMM creates a piecewise multilinear representation of uniformly sampled scalar data and can selectively relax or enforce constraints on conformity, continuity, and coverage, delivering a flexible adaptive representation. AMM also supports representing the function using mixed-precision values to further the achievable gains in data reduction. We describe a practical approach to creating AMM incrementally using arbitrary orderings of data and demonstrate AMM on six types of resolution and precision datastreams. By interfacing with state-of-the-art rendering tools through VTK, we demonstrate the practical and computational advantages of our representation for visualization techniques. With an open-source release of our tool to create AMM, we make such evaluation of data reduction accessible to the community, which we hope will foster new opportunities and future data reduction schemes.

97 MATHEMATICS AND COMPUTING↗

INITIATORS AND TRIGGERING CONDITIONS FOR ADAPTIVE AUTOMATION IN ADVANCED SMALL MODULAR REACTORS

It is anticipated that Advanced Small Modular Reactors (AdvSMRs) will employ high degrees of automation. High levels of automation can enhance system performance, but often at the cost of reduced human performance. Automation can lead to human out-of the loop issues, unbalanced workload, complacency, and other problems if it is not designed properly. Researchers have proposed adaptive automation (defined as dynamic or flexible allocation of functions) as a way to get the benefits of higher levels of automation without the human performance costs. Adaptive automation has the potential to balance operator workload and enhance operator situation awareness by allocating functions to the operators in a way that is sensitive to overall workload and capabilities at the time of operation. However, there still a number of questions regarding how to effectively design adaptive automation to achieve that potential. One of those questions is related to how to initiate (or trigger) a shift in automation in order to provide maximal sensitivity to operator needs without introducing undesirable consequences (such as unpredictable mode changes). Several triggering mechanisms for shifts in adaptive automation have been proposed including: operator initiated, critical events, performance-based, physiological measurement, model-based, and hybrid methods. As part of a larger project to develop design guidance for human-automation collaboration in AdvSMRs, researchers at Idaho National Laboratory have investigated the effectiveness and applicability of each of these triggering mechanisms in the context of AdvSMR. Researchers reviewed the empirical literature on adaptive automation and assessed each triggering mechanism based on the human-system performance consequences of employing that mechanism. Researchers also assessed the practicality and feasibility of using the mechanism in the context of an AdvSMR control room. Results indicate that there are tradeoffs associated with each mechanism, but that some are more applicable to the AdvSMR domain. The two mechanisms that consistently improve performance in laboratory studies are operator initiated adaptive automation based on hierarchical task delegation and the Electroencephalogram(EEG) –based measure of engagement. Current EEG methods are intrusive and require intensive analysis; therefore it is not recommended for an AdvSMR control rooms at this time. Researchers also discuss limitations in the existing empirical literature and make recommendations for further research.

99 GENERAL AND MISCELLANEOUS↗

Cold adaptation of the mononuclear molybdoenzyme periplasmic nitrate reductase from the Antarctic bacterium Shewanella gelidimarina

Highlights: Black-Right-Pointing-Pointer Cold-adapted phenotype of NapA from the Antarctic bacterium Shewanella gelidimarina. Black-Right-Pointing-Pointer Protein homology model of NapA from S. gelidimarina and mesophilic homologue. Black-Right-Pointing-Pointer Six amino acid residues identified as lead candidates governing NapA cold adaptation. Black-Right-Pointing-Pointer Molecular-level understanding of designing cool-temperature in situ oxyanion sensors. -- Abstract: The reduction of nitrate to nitrite is catalysed in bacteria by periplasmic nitrate reductase (Nap) which describes a system of variable protein subunits encoded by the nap operon. Nitrate reduction occurs in the NapA subunit, which contains a bis-molybdopterin guanine dinucleotide (Mo-MGD) cofactor and one [4Fe-4S] iron-sulfur cluster. The activity of periplasmic nitrate reductase (Nap) isolated as native protein from the cold-adapted (psychrophilic) Antarctic bacterium Shewanella gelidimarina (Nap{sub Sgel}) and middle-temperature adapted (mesophilic) Shewanella putrefaciens (Nap{sub Sput}) was examined at varied temperature. Irreversible deactivation of Nap{sub Sgel} and Nap{sub Sput} occurred at 54.5 and 65 Degree-Sign C, respectively. When Nap{sub Sgel} was preincubated at 21-70 Degree-Sign C for 30 min, the room-temperature nitrate reductase activity was maximal and invariant between 21 and 54 Degree-Sign C, which suggested that Nap{sub Sgel} was poised for optimal catalysis at modest temperatures and, unlike Nap{sub Sput}, did not benefit from thermally-induced refolding. At 20 Degree-Sign C, Nap{sub Sgel} reduced selenate at 16% of the rate of nitrate reduction. Nap{sub Sput} did not reduce selenate. Sequence alignment showed 46 amino acid residue substitutions in Nap{sub Sgel} that were conserved in NapA from mesophilic Shewanella, Rhodobacter and Escherichia species and could be associated with the Nap{sub Sgel} cold-adapted phenotype. Protein homology modeling of Nap{sub Sgel} using a mesophilic template with 66% amino acid identity showed the majority of substitutions occurred at the protein surface distal to the Mo-MGD cofactor. Two mesophilic {r_reversible} psychrophilic substitutions (Asn {r_reversible} His, Val {r_reversible} Trp) occurred in a region close to the surface of the NapA substrate funnel resulting in potential interdomain {pi}-{pi} and/or cation-{pi} interactions. Three mesophilic {r_reversible} psychrophilic substitutions occurred within 4.5 A of the Mo-MGD cofactor (Phe {r_reversible} Met, Ala {r_reversible} Ser, Ser {r_reversible} Thr) resulting in local regions that varied in hydrophobicity and hydrogen bonding networks. These results contribute to the understanding of thermal protein adaptation in a redox-active mononuclear molybdenum enzyme and have implications in optimizing the design of low-temperature environmental biosensors.

60 APPLIED LIFE SCIENCES↗

Interactions between climate change mitigation and adaptation: The case of hydropower in Brazil

In this paper we perform a multi-model comparison to assess strategies for adaptation to climate change impacts in hydropower generation in Brazil under two Representative Concentration Pathways (RCPs 4.5 and 8.5). The approach used allows for evaluating the interactions between climate change mitigation and adaptation strategies under low and high impact scenarios through 2050. Climate change impact projections of sixteen General Circulation Models (GCMs) indicate that a global emissions trajectory of an RCP 8.5 scenario would likely yield more severe impacts on hydropower generation than an RCP 4.5 scenario. Adaptation modeling suggests that climate change impacts can be compensated by a wide range of alternatives, whose optimality will depend on the level of mitigation effort pursued. Our results show that climate change impacts would lead to even higher emissions in the absence of climate change mitigation policies. On the other hand, mitigation strategies to pursue lower emissions are maintained under climate change impact shocks, meaning that mitigation strategies are robust when faced with adaptation challenges. Mitigation efforts could yield a more diverse and less carbon intensive mix of technological options for adaptation. When including investment costs to adapt to climate change impacts, in some cases mitigation can lead to a lower total investment level.

Lucena, André F. P.↗

A Clinical Concept for Interfractional Adaptive Radiation Therapy in the Treatment of Head and Neck Cancer

Purpose: To present an approach to fast, interfractional adaptive RT in intensity-modulated radiation therapy (IMRT) of head and neck tumors in clinical routine. Ensuring adequate patient position throughout treatment proves challenging in high-precision RT despite elaborate immobilization. Because of weight loss, treatment plans must be adapted to account for requiring supportive therapy incl. feeding tube or parenteral nutrition without treatment breaks. Methods and Materials: In-room CT position checks are used to create adapted IMRT treatment plans by stereotactic correlation to the initial setup, and volumes are adapted to the new geometry. New IMRT treatment plans are prospectively created on the basis of position control scans using the initial optimization parameters in KonRad without requiring complete reoptimization and thus facilitating quick replanning in daily routine. Patients treated for squamous cell head and neck cancer (SCCHN) in 2006-2007 were evaluated as to necessity/number of replannings, weight loss, dose, and plan parameters. Results: Seventy-two patients with SCCHN received IMRT to the primary site and lymph nodes (median dose 70.4 Gy). All patients received concomitant chemotherapy requiring supportive therapy by feeding tube or parenteral nutrition. Median weight loss was 7.8 kg, median volume loss was approximately 7%. Fifteen of 72 patients required adaptation of their treatment plans at least once. Target coverage was improved by up to 10.7% (median dose). The increase of dose to spared parotid without replanning was 11.7%. Replanning including outlining and optimization was feasible within 2 hours for each patient, and treatment could be continued without any interruptions. Conclusion: To preserve high-quality dose application, treatment plans must be adapted to anatomical changes. Replanning based on position control scans therefore presents a practical approach in clinical routine. In the absence of clinically usable online correction methods, this approach allows significant improvement of target volume coverage and continuous parotid sparing without treatment delays.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Longitudinal Investigation of Adaptive Functioning Following Conformal Irradiation for Pediatric Craniopharyngioma and Low-Grade Glioma

Purpose: Children treated for brain tumors with conformal radiation therapy experience preserved cognitive outcomes. Early evidence suggests that adaptive functions or independent-living skills may be spared. This longitudinal investigation prospectively examined intellectual and adaptive functioning during the first 5 years following irradiation for childhood craniopharyngioma and low-grade glioma (LGG). The effect of visual impairment on adaptive outcomes was investigated. Methods and Materials: Children with craniopharyngioma (n=62) and LGG (n=77) were treated using conformal or intensity modulated radiation therapy. The median age was 8.05 years (3.21-17.64 years) and 8.09 years (2.20-19.27 years), respectively. Serial cognitive evaluations including measures of intelligence quotient (IQ) and the Vineland Adaptive Behavior Scales (VABS) were conducted at preirradiation baseline, 6 months after treatment, and annually through 5 years. Five hundred eighty-eight evaluations were completed during the follow-up period. Results: Baseline assessment revealed no deficits in IQ and VABS indices for children with craniopharyngioma, with significant (P<.05) longitudinal decline in VABS Communication and Socialization indices. Clinical factors associated with more rapid decline included females and preirradiation chemotherapy (interferon). The only change in VABS Daily Living Skills correlated with IQ change (r=0.34; P=.01) in children with craniopharyngioma. Children with LGG performed below population norms (P<.05) at baseline on VABS Communication, Daily Living Indices, and the Adaptive Behavior Composite, with significant (P<.05) longitudinal decline limited to VABS Communication. Older age at irradiation was a protective factor against longitudinal decline. Severe visual impairment did not independently correlate with poorer adaptive outcomes for either tumor group. Conclusions: There was relative sparing of postirradiation functional outcomes over time in this sample. Baseline differences in functional abilities before the initiation of irradiation suggested that other factors influence functional outcomes above and beyond the effects of irradiation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Adaptive planning using megavoltage fan-beam CT for radiation therapy with testicular shielding

This study highlights the use of adaptive planning to accommodate testicular shielding in helical tomotherapy for malignancies of the proximal thigh. Two cases of young men with large soft tissue sarcomas of the proximal thigh are presented. After multidisciplinary evaluation, preoperative radiation therapy was recommended. Both patients were referred for sperm banking and lead shields were used to minimize testicular dose during radiation therapy. To minimize imaging artifacts, kilovoltage CT (kVCT) treatment planning was conducted without shielding. Generous hypothetical contours were generated on each 'planning scan' to estimate the location of the lead shield and generate a directionally blocked helical tomotherapy plan. To ensure the accuracy of each plan, megavoltage fan-beam CT (MVCT) scans were obtained at the first treatment and adaptive planning was performed to account for lead shield placement. Two important regions of interest in these cases were femurs and femoral heads. During adaptive planning for the first patient, it was observed that the virtual lead shield contour on kVCT planning images was significantly larger than the actual lead shield used for treatment. However, for the second patient, it was noted that the size of the virtual lead shield contoured on the kVCT image was significantly smaller than the actual shield size. Thus, new adaptive plans based on MVCT images were generated and used for treatment. The planning target volume was underdosed up to 2% and had higher maximum doses without adaptive planning. In conclusion, the treatment of the upper thigh, particularly in young men, presents several clinical challenges, including preservation of gonadal function. In such circumstances, adaptive planning using MVCT can ensure accurate dose delivery even in the presence of high-density testicular shields.

61 RADIATION PROTECTION AND DOSIMETRY↗

Sim-to-real supervised domain adaptation for radioisotope identification

Machine learning has the potential to improve the speed and reliability of radioisotope identification using gamma spectroscopy. However, meticulously labeling an experimental dataset for training is often prohibitively expensive, while training models purely on synthetic data is risky due to the domain gap between simulated and experimental measurements. In this research, we demonstrate that supervised domain adaptation can substantially improve the performance of radioisotope identification models by transferring knowledge between synthetic and experimental data domains. We consider two domain adaptation scenarios: (1) a simulation-to-simulation adaptation, where we perform multi-label proportion estimation using simulated high-purity germanium detectors, and (2) a simulation-to-experimental adaptation, where we perform multi-class, single-label classification using measured spectra from handheld lanthanum bromide (LaBr) and sodium iodide (NaI) detectors. We begin by pretraining a spectral classifier on synthetic data using a custom transformer-based neural network. After subsequent fine-tuning on just 64 labeled experimental spectra, we achieve a test accuracy of 96% in the sim-to-real scenario with a LaBr detector, far surpassing a synthetic-only baseline model (75%) and a model trained from scratch (80%) on the same 64 spectra. Furthermore, we demonstrate that domain-adapted models learn more human-interpretable features than experiment-only baseline models. Overall, our results highlight the potential for supervised domain adaptation techniques to bridge the sim-to-real gap in radioisotope identification, enabling the development of accurate and explainable classifiers even in real-world scenarios where access to experimental data is limited.

Lalor, Peter W.↗

Adaptive ensemble simulations of biomolecules

Recent advances in both theory and computational power have created opportunities to simulate biomolecular processes more efficiently using adaptive ensemble simulations. Ensemble simulations are now widely used to compute a number of individual simulation trajectories and analyze statistics across them. Adaptive ensemble simulations offer a further level of sophistication and flexibility by enabling highlevel algorithms to control simulations-based on intermediate results. We review some of the adaptive ensemble algorithms and software infrastructure currently in use and outline where the complexities of implementing adaptive simulation have limited algorithmic innovation to date. In conclusion, we describe an adaptive ensemble API to overcome some of these barriers and more flexibly and simply express adaptive simulation algorithms to help realize the power of this type of simulation.

97 MATHEMATICS AND COMPUTING↗

Thermal Weight Determination and Interstate Coupling in State-Averaged ADAPT-VQE

Characterizing electronic thermal states at low temperatures is an important but challenging task in quantum chemistry and condensed matter physics, making it a prime candidate for a useful application in quantum computing. One of the most successful methods for state preparation on quantum computers is the Adaptive, Problem-Tailored (ADAPT) Variational Quantum Eigensolver (VQE), which has recently been generalized to treat excited states within a state-averaged framework as well as Gibbs states. In this work, we introduce Helmholtz-Optimized Thermal (HOT) ADAPT-VQE, an ancilla-free strategy for preparing Gibbs states that directly minimizes the Helmholtz free energy by targeting the dominant eigenstates of the thermal ensemble. We demonstrate the usefulness of HOT-ADAPT-VQE by predicting the free energy of two model systems with strongly correlated ground states: (1) the Fe 2+ cation in a magnetic field and (2) a [Cu 2 O 7 ] 10– fragment of the Mott insulator La 2 CuO 4 . Our results demonstrate that HOT-ADAPT-VQE significantly improves upon Gibbs-state estimates from multistate variants of ADAPT-VQE, often with substantially shallower quantum circuits, making it a promising candidate for thermal-state calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Adaptive, problem-tailored variational quantum eigensolver mitigates rough parameter landscapes and barren plateaus

Abstract Variational quantum eigensolvers (VQEs) represent a powerful class of hybrid quantum-classical algorithms for computing molecular energies. Various numerical issues exist for these methods, however, including barren plateaus and large numbers of local minima. In this work, we consider the Adaptive, Problem-Tailored Variational Quantum Eiegensolver (ADAPT-VQE) ansätze, and examine how they are impacted by these local minima. We find that while ADAPT-VQE does not remove local minima, the gradient-informed, one-operator-at-a-time circuit construction accomplishes two things: First, it provides an initialization strategy that can yield solutions with over an order of magnitude smaller error compared to random initialization, and which is applicable in situations where chemical intuition cannot help with initialization, i.e., when Hartree-Fock is a poor approximation to the ground state. Second, even if an ADAPT-VQE iteration converges to a local trap at one step, it can still “burrow” toward the exact solution by adding more operators, which preferentially deepens the occupied trap. This same mechanism helps highlight a surprising feature of ADAPT-VQE: It should not suffer optimization problems due to barren plateaus and random initialization. Even if such barren plateaus appear in the parameter landscape, our analysis suggests that ADAPT-VQE avoids such regions by design.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Functional team selection as a framework for local adaptation in plants and their belowground microbiomes

Abstract Multicellular organisms are hosts to diverse communities of smaller organisms known as microbiomes. Plants have distinctive microbiomes that can provide important functions related to nutrition, defense, and stress tolerance. Empirical studies provide convincing evidence that in some—but not all—circumstances, belowground microbiomes help plants adapt to their local environment. The purpose of this review is to develop functional team selection (FTS) as a framework to help predict the conditions necessary for root microbiomes to generate local adaptation for their plant hosts. FTS envisions plants and their microbiomes as complex adaptive systems, and plant adaptations as emergent properties of these systems. If plants have the capacity to recognize and cultivate beneficial microbes and suppress pathogens, then it is possible for plants to evolve the capacity to gain adaptations by curating their microbiome. In resource-limited and stressful environments, the emergent functions of complex microbial systems may contribute to positive feedback linked to plant vigor, and ultimately, local adaptation. The key factors in this process are: (i) selective force, (ii) host constitution, (iii) microbial diversity, and (iv) time. There is increasing interest in harnessing beneficial microbial interactions in agriculture and many microbial growth-promoting products are commercially available, but their use is controversial because a large proportion of these products fail to consistently enhance plant growth. The FTS framework may help direct the development of durable plant-microbiome systems that enhance crop production and diminish pathogens. It may also provide valuable insights for understanding and managing other kinds of host-microbe systems.

Environmental Sciences & Ecology↗

Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks

Here we propose two approaches of locally adaptive activation functions namely, layer-wise and neuron-wise locally adaptive activation functions, which improve the performance of deep and physics-informed neural networks. The local adaptation of activation function is achieved by introducing a scalable parameter in each layer (layer-wise) and for every neuron (neuron-wise) separately, and then optimizing it using a variant of stochastic gradient descent algorithm. In order to further increase the training speed, an activation slope-based slope recovery term is added in the loss function, which further accelerates convergence, thereby reducing the training cost. On the theoretical side, we prove that in the proposed method, the gradient descent algorithms are not attracted to sub-optimal critical points or local minima under practical conditions on the initialization and learning rate, and that the gradient dynamics of the proposed method is not achievable by base methods with any (adaptive) learning rates. We further show that the adaptive activation methods accelerate the convergence by implicitly multiplying conditioning matrices to the gradient of the base method without any explicit computation of the conditioning matrix and the matrix–vector product. The different adaptive activation functions are shown to induce different implicit conditioning matrices. Furthermore, the proposed methods with the slope recovery are shown to accelerate the training process.

97 MATHEMATICS AND COMPUTING↗

Irradiation of the prostate and pelvic lymph nodes with an adaptive algorithm

Purpose: The simultaneous treatment of pelvic lymph nodes and the prostate in radiotherapy for prostate cancer is complicated by the independent motion of these two target volumes. In this work, the authors study a method to adapt intensity modulated radiation therapy (IMRT) treatment plans so as to compensate for this motion by adaptively morphing the multileaf collimator apertures and adjusting the segment weights. Methods: The study used CT images, tumor volumes, and normal tissue contours from patients treated in our institution. An IMRT treatment plan was then created using direct aperture optimization to deliver 45 Gy to the pelvic lymph nodes and 50 Gy to the prostate and seminal vesicles. The prostate target volume was then shifted in either the anterior-posterior direction or in the superior-inferior direction. The treatment plan was adapted by adjusting the aperture shapes with or without re-optimizing the segment weighting. The dose to the target volumes was then determined for the adapted plan. Results: Without compensation for prostate motion, 1 cm shifts of the prostate resulted in an average decrease of 14% in D-95%. If the isocenter is simply shifted to match the prostate motion, the prostate receives the correct dose but the pelvic lymph nodes are underdosed by 14% {+-} 6%. The use of adaptive morphing (with or without segment weight optimization) reduces the average change in D-95% to less than 5% for both the pelvic lymph nodes and the prostate. Conclusions: Adaptive morphing with and without segment weight optimization can be used to compensate for the independent motion of the prostate and lymph nodes when combined with daily imaging or other methods to track the prostate motion. This method allows the delivery of the correct dose to both the prostate and lymph nodes with only small changes to the dose delivered to the target volumes.

62 RADIOLOGY AND NUCLEAR MEDICINE↗