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At least 289 records · Page 16

A prototype combinatorial scheduler for space-borne observational instruments

A prototype scheduler has been developed for observations to be carried out by the NASA Gamma Ray Observatory. The purpose of the scheduling system is to provide an automated method to determine optimized schedules conforming to all scheduling guidelines. Strategies used in creating COMPTEL/EGRET schedules and in scheduling OSSE targets are examined. It is concluded that the hybrid scheduler creates optimal schedules, or very nearly optimal schedules, and reduces the estimated scheduling time by 18 or 19 orders of magnitude, from about 10 to the 14th years to as few as 561 seconds.

Gilstrap, Lewey O.↗

DIVA V2.0

The DIVA software interfaces a process in which researchers design their DNA with a web-based graphical user interface, submit their designs to a central queue, and a few weeks later receive their sequence-verified clonal constructs. Each researcher independently designs the DNA to be constructed with a web-based BioCAD tool, and presses a button to submit their designs to a central queue. Researchers have web-based access to their DNA design queues, and can track the progress of their submitted designs as they progress from "evaluation", to "waiting for reagents", to "in progress", to "complete". Researchers access their completed constructs through the central DNA repository. Along the way, all DNA construction success/failure rates are captured in a central database. Once a design has been submitted to the queue, a small number of dedicated staff evaluate the design for feasibility and provide feedback to the responsible researcher if the design is either unreasonable (e.g., encompasses a combinatorial library of a billion constructs) or small design changes could significantly facilitate the downstream implementation process. The dedicated staff then use DNA assembly design automation software to optimize the DNA construction process for the design, leveraging existing parts from the DNA repository where possible and ordering synthetic DNA where necessary. SynTrack software manages the physical locations and availability of the various requisite reagents and process inputs (e.g., DNA templates). Once all requisite process inputs are available, the design progresses from "waiting for reagents" to "in progress" in the design queue. Human-readable and machine-parseable DNA construction protocols output by the DNA assembly design automation software are then executed by the dedicated staff exploiting lab automation devices wherever possible. Since the all employed DNA construction methods are sequence-agnostic, standardized (utilize the same enzymatic master mixes and reaction conditions), completely independent DNA construction tasks can be aggregated into the same multi-well plates and pursued in parallel. The resulting sets of cloned constructs can then be screened by high-throughput next-gen sequencing platforms for sequence correctness. A combination of long read-length (e.g., PacBio) and paired-end read platforms (e.g., Illumina) would be exploited depending the particular task at hand (e.g., PacBio might be sufficient to screen a set of pooled constructs with significant gene divergence). Post sequence verification, designs for which at least one correct clone was identified will progress to a "complete" status, while designs for which no correct clones wereidentified will progress to a "failure" status. Depending on the failure mode (e.g., no transformants), and how many prior attempts/variations of assembly protocol have been already made for a given design, subsequent attempts may be made or the design can progress to a "permanent failure" state. All success and failure rate information will be captured during the process, including at which stage a given clonal construction procedure failed (e.g., no PCR product) and what the exact failure was (e.g. assembly piece 2 missing). This success/failure rate data can be leveraged to refine the DNA assembly design process.

CHEN, JOANNA↗

Optimizing Muscle Parameters in Musculoskeletal Modeling Using Monte Carlo Simulations

Astronauts assigned to long-duration missions experience bone and muscle atrophy in the lower limbs. The use of musculoskeletal simulation software has become a useful tool for modeling joint and muscle forces during human activity in reduced gravity as access to direct experimentation is limited. Knowledge of muscle and joint loads can better inform the design of exercise protocols and exercise countermeasure equipment. In this study, the LifeModeler(TM) (San Clemente, CA) biomechanics simulation software was used to model a squat exercise. The initial model using default parameters yielded physiologically reasonable hip-joint forces. However, no activation was predicted in some large muscles such as rectus femoris, which have been shown to be active in 1-g performance of the activity. Parametric testing was conducted using Monte Carlo methods and combinatorial reduction to find a muscle parameter set that more closely matched physiologically observed activation patterns during the squat exercise. Peak hip joint force using the default parameters was 2.96 times body weight (BW) and increased to 3.21 BW in an optimized, feature-selected test case. The rectus femoris was predicted to peak at 60.1% activation following muscle recruitment optimization, compared to 19.2% activation with default parameters. These results indicate the critical role that muscle parameters play in joint force estimation and the need for exploration of the solution space to achieve physiologically realistic muscle activation.

Hanson, Andrea↗

A Method for Aircraft Concept Selection Using Multicriteria Interactive Genetic Algorithms

The problem of aircraft concept selection has become increasingly difficult in recent years as a result of a change from performance as the primary evaluation criteria of aircraft concepts to the current situation in which environmental effects, economics, and aesthetics must also be evaluated and considered in the earliest stages of the decision-making process. This has prompted a shift from design using historical data regression techniques for metric prediction to the use of physics-based analysis tools that are capable of analyzing designs outside of the historical database. The use of optimization methods with these physics-based tools, however, has proven difficult because of the tendency of optimizers to exploit assumptions present in the models and drive the design towards a solution which, while promising to the computer, may be infeasible due to factors not considered by the computer codes. In addition to this difficulty, the number of discrete options available at this stage may be unmanageable due to the combinatorial nature of the concept selection problem, leading the analyst to arbitrarily choose a sub-optimum baseline vehicle. These concept decisions such as the type of control surface scheme to use, though extremely important, are frequently made without sufficient understanding of their impact on the important system metrics because of a lack of computational resources or analysis tools. This paper describes a hybrid subjective/quantitative optimization method and its application to the concept selection of a Small Supersonic Transport. The method uses Genetic Algorithms to operate on a population of designs and promote improvement by varying more than sixty parameters governing the vehicle geometry, mission, and requirements. In addition to using computer codes for evaluation of quantitative criteria such as gross weight, expert input is also considered to account for criteria such as aeroelasticity or manufacturability which may be impossible or too computationally expensive to consider explicitly in the analysis. Results indicate that concepts resulting from the use of this method represent designs which are promising to both the computer and the analyst, and that a mapping between concepts and requirements that would not otherwise be apparent is revealed.

Buonanno, Michael↗

Discovery of Fe–Ce Oxide/BiVO 4 Photoanodes through Combinatorial Exploration of Ni–Fe–Co–Ce Oxide Coatings

An efficient photoanode is a prerequisite for a viable solar fuels technology. The challenges to realizing an efficient photoanode include the integration of a semiconductor light absorber and a metal oxide electrocatalyst to optimize corrosion protection, light trapping, hole transport, and photocarrier recombination sites. In order to efficiently explore metal oxide coatings, we employ a high throughput methodology wherein a uniform BiVO 4 film is coated with 858 unique metal oxide coatings covering a range of metal oxide loadings and the full (Ni-Fe-Co-Ce)O x pseudo-quaternary composition space. Photoelectrochemical characterization of the photoanodes reveals that specific combinations of metal oxide composition and loading provide up to a 13-fold increase in the maximum photoelectrochemical power generation for oxygen evolution in pH 13 electrolyte. Through mining of the high throughput data we identify composition regions that form improved interfaces with BiVO 4 . Of particular note, integrated photoanodes with catalyst compositions in the range Fe (0.4-0.6) Ce (0.6-0.4) O x exhibit high interface quality and excellent photoelectrochemical power conversion. Furthermore, for scaled-up inkjet-printed electrodes and photoanodic electrodeposition of this composition on BiVO 4 we can confirm the discovery and the synthesis-independent interface improvement of (Fe-Ce)O x coatings on BiVO 4 .

14 SOLAR ENERGY↗

Performance Prediction of High‐Entropy Perovskites La 0.8 Sr 0.2 Mn x Co y Fe z O 3 with Automated High‐Throughput Characterization of Combinatorial Libraries and Machine Learning

Perovskite oxides form a large family of materials with applications across various fields, owing to their structural and chemical flexibility. Efficient exploration of this extensive compositional space is now achievable through automated high-throughput experimentation combined with machine learning. In this study, we investigate the composition–structure–performance relationships of high-entropy La 0.8 Sr 0.2 Mn x Co y Fe z O 3±𝞭 perovskite oxides (0 < x, y, z <1; x+y+z≈1) for application as oxygen electrodes in Solid Oxide Cells. Following the deposition of a continuous compositional map using thin-film combinatorial pulsed laser deposition, compositional, structural, and performance properties are characterized using six different techniques with mapping capabilities. Random forests effectively model electrochemical performance, consistently identifying Fe-rich oxides as optimal compounds with the lowest area-specific resistance values for oxygen electrodes at 700 °C. Additionally, the models identify a statistical correlation between oxygen sublattice distortion—derived from spectral analysis of Raman-active modes—and enhanced performance.

high entropy oxides↗

Development of Multimaterial Graded Structure from Superalloys to Refractory Alloys

This research focused on developing hybrid microstructures for extreme environments where zone-specific performance is essential. Traditional structural applications rely on single alloy compositions, often requiring compromises in cost, corrosion resistance, or high-temperature stability, while dissimilar welding leads to abrupt material transitions and potential mechanical weaknesses. By employing a combinatorial approach, this study enabled a gradual transition of chemistries across components, allowing site-specific tailoring of microstructures. Leveraging additive manufacturing’s flexibility, this concept facilitated multi-material alloying and optimized fabrication strategies, unlocking new capabilities in high-performance applications while reducing manufacturing costs.

99 GENERAL AND MISCELLANEOUS↗

Development of Multimaterial Graded Structure from Superalloys to Refractory Alloys

This research focused on developing hybrid microstructures for extreme environments where zone-specific performance is essential. Traditional structural applications rely on single alloy compositions, often requiring compromises in cost, corrosion resistance, or high-temperature stability, while dissimilar welding leads to abrupt material transitions and potential mechanical weaknesses. By employing a combinatorial approach, this study enabled a gradual transition of chemistries across components, allowing site-specific tailoring of microstructures. Leveraging additive manufacturing’s flexibility, this concept facilitated multi-material alloying and optimized fabrication strategies, unlocking new capabilities in high-performance applications while reducing manufacturing costs.

36 MATERIALS SCIENCE↗

Optimizing the violacein biosynthetic pathway using droplet microfluidics

© 17CBMS-0001. We have developed a microfluidic platform that uses aqueous droplets suspended in an oil phase as discrete reaction chambers to carry out molecular biology steps. It integrates and automates all critical procedures of synthetic biology including combinatorial gene assembly, transformation by electroporation, addition of selection medium, culture, assay and sorting on a single device.

Gach, PC↗

Fast Solution in Sparse LDA for Binary Classification

An algorithm that performs sparse linear discriminant analysis (Sparse-LDA) finds near-optimal solutions in far less time than the prior art when specialized to binary classification (of 2 classes). Sparse-LDA is a type of feature- or variable- selection problem with numerous applications in statistics, machine learning, computer vision, computational finance, operations research, and bio-informatics. Because of its combinatorial nature, feature- or variable-selection problems are NP-hard or computationally intractable in cases involving more than 30 variables or features. Therefore, one typically seeks approximate solutions by means of greedy search algorithms. The prior Sparse-LDA algorithm was a greedy algorithm that considered the best variable or feature to add/ delete to/ from its subsets in order to maximally discriminate between multiple classes of data. The present algorithm is designed for the special but prevalent case of 2-class or binary classification (e.g. 1 vs. 0, functioning vs. malfunctioning, or change versus no change). The present algorithm provides near-optimal solutions on large real-world datasets having hundreds or even thousands of variables or features (e.g. selecting the fewest wavelength bands in a hyperspectral sensor to do terrain classification) and does so in typical computation times of minutes as compared to days or weeks as taken by the prior art. Sparse LDA requires solving generalized eigenvalue problems for a large number of variable subsets (represented by the submatrices of the input within-class and between-class covariance matrices). In the general (fullrank) case, the amount of computation scales at least cubically with the number of variables and thus the size of the problems that can be solved is limited accordingly. However, in binary classification, the principal eigenvalues can be found using a special analytic formula, without resorting to costly iterative techniques. The present algorithm exploits this analytic form along with the inherent sequential nature of greedy search itself. Together this enables the use of highly-efficient partitioned-matrix-inverse techniques that result in large speedups of computation in both the forward-selection and backward-elimination stages of greedy algorithms in general.

Moghaddam, Baback↗

Structure-based design of prefusion-stabilized human metapneumovirus fusion proteins

The human metapneumovirus (hMPV) fusion (F) protein is essential for viral entry and is a key target of neutralizing antibodies and vaccine development. The prefusion conformation is thought to be the optimal vaccine antigen, but previously described prefusion F proteins expressed poorly and were not well stabilized. Here, we use structures of hMPV F to guide the design of 42 variants containing stabilizing substitutions. Through combinatorial addition of disulfide bonds, cavity-filling substitutions, and improved electrostatic interactions, we describe a prefusion-stabilized F protein (DS-CavEs2) that expresses at 15mg/L and has a melting temperature of 71.9°C. Crystal structures of two prefusion-stabilized hMPV F variants reveal that antigenic surfaces are largely unperturbed. Importantly, immunization of mice with DS-CavEs2 elicits significantly higher neutralizing antibody titers against hMPV A1 and B1 viruses than postfusion F. The improved properties of DS-CavEs2 will advance the development of hMPV vaccines and the isolation of therapeutic antibodies.

59 BASIC BIOLOGICAL SCIENCES↗

Trade-Offs in Thin Film Solar Cells with Layered Chalcostibite Photovoltaic Absorbers

Discovery of novel semiconducting materials is needed for solar energy conversion and other optoelectronic applications. However, emerging low-dimensional solar absorbers often have unconventional crystal structures and unusual combinations of optical absorption and electrical transport properties, which considerably slows down the research and development progress. Here, the effect of stronger absorption and weaker carrier collection of 2D-like absorber materials are studied using a high-throughput combinatorial experimental approach, complemented by advanced characterization and computations. It is found that the photoexcited charge carrier collection in CuSbSe 2 solar cells is enhanced by drift in an electric field, addressing a different absorption/collection balance. The resulting drift solar cells efficiency is <5% due to inherent J SC / V OC trade-off, suggesting that improved carrier diffusion and better contacts are needed to further increase the CuSbSe 2 performance. Furthermore, this study also illustrates the advantages of high-throughput experimental methods for fast optimization of the optoelectronic devices based on emerging low-dimensional semiconductor materials.

14 SOLAR ENERGY↗

4-1BB Aptamer-Based Immunomodulation Enhances the Therapeutic Index of Radiation Therapy in Murine Tumor Models

Purpose: To report a novel strategy using oligonucleotide aptamers to 4-1BB as an alternate method for costimulation, and show that combinatorial therapy with radiation improves the therapeutic ratio over equivalent monoclonal antibodies. Methods and Materials: Subcutaneous 4T1 (mouse mammary carcinoma) tumors were established (approximately 100 mm{sup 3}), and a radiation therapy (RT) dose/fractionation schedule that optimally synergizes with 4-1BB monoclonal antibody (mAb) was identified. Comparable tumor control and animal survival was observed when either 4-1BB antibody or aptamer were combined with RT using models of breast cancer and melanoma (4T1 and B16-F10). Off-target CD8{sup +} T-cell toxicity was evaluated by quantification of CD8{sup +} T cells in livers and spleens of treated animals. Results: When combined with 4-1BB mAb, significant differences in tumor control were observed by varying RT dose and fractionation schedules. Optimal synergy between RT and 4-1BB mAb was observed at 5 Gy × 6. Testing 4-1BB mAb and aptamer independently using the optimal RT (5 Gy × 6 for 4T1/Balb/c and 12 Gy × 1 for B16/C57BL6J mouse models) revealed equivalent tumor control using 4-1BB aptamer and 4-1BB mAb. 4-1BB mAb, but not 4-1BB aptamer-treated animals, exhibited increased lymphocytic liver infiltrates and increased splenic and liver CD8{sup +} T cells. Conclusions: Radiation therapy synergizes with 4-1BB mAb, and this effect is dependent on RT dose and fractionation. Tumor control by 4-1BB aptamer is equivalent to 4-1BB mAb when combined with optimal RT dose, without eliciting off-target liver and spleen CD8{sup +} expansion. 4-1BB aptamer-based costimulation affords a comparable and less toxic strategy to augment RT-mediated tumor control.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

SpectralFly: Ramanujan Graphs as Flexible and Efficient Interconnection Networks

In recent years, graph theoretic considerations have become increasingly important in the design of HPC interconnection topologies. One approach is to seek optimal or near-optimal families of graphs with respect to a particular graph theoretic property, such as diameter. For example, the SlimFly topology is based on a construction of McKay, Miller, and \v{S}ir\'{a}\v{n} which produces a diameter two graph on a number of nodes approaching the Moore bound, i.e. the largest possible diameter two graph with a fixed radix. Motivated by recent work of Aksoy, Bruillard, Young, and Raugas, we consider topologies which optimize the spectral gap rather than the diameter. In particular, we introduce a novel HPC topology, SpectralFly, designed around the Ramanujan graph construction of Lubotzky, Phillips, and Sarnak (LPS). In this work, we show that the combinatorial properties, such as diameter, bisection bandwidth, average path length, and resilience to link failure, of SpectralFly topologies are better than, or comparable to, similarly constrained DragonFly, SlimFly, and BundleFly topologies. Additionally, we simulate the performance of SpectralFly topologies on a representative sample of physics-inspired HPC workloads using the Structure Simulation Toolkit Macroscale Element Library simulator and demonstrate considerable benefit to using LPS construction as the basis of the SpectralFly topology.

graphs and networks, network topology, interconnec↗

Drosophila Nanos acts as a molecular clamp that modulates the RNA-binding and repression activities of Pumilio

Collaboration among the multitude of RNA-binding proteins (RBPs) is ubiquitous, yet our understanding of these key regulatory complexes has been limited to single RBPs. We investigated combinatorial translational regulation by Drosophila Pumilio (Pum) and Nanos (Nos), which control development, fertility, and neuronal functions. Our results show how the specificity of one RBP (Pum) is modulated by cooperative RNA recognition with a second RBP (Nos) to synergistically repress mRNAs. Crystal structures of Nos-Pum-RNA complexes reveal that Nos embraces Pum and RNA, contributes sequence-specific contacts, and increases Pum RNA-binding affinity. Nos shifts the recognition sequence and promotes repression complex formation on mRNAs that are not stably bound by Pum alone, explaining the preponderance of sub-optimal Pum sites regulated in vivo . Our results illuminate the molecular mechanism of a regulatory switch controlling crucial gene expression programs, and provide a framework for understanding how the partnering of RBPs evokes changes in binding specificity that underlie regulatory network dynamics.

Weidmann, Chase A.↗

Developing Multi-Gene CRISPRa/I Programs to Accelerate DBTL Cycles in ABF Hosts Engineered for Chemical Production (CRADA 468)

Bacterial metabolism is comprised of large and complex gene networks that can produce valuable chemical products. Sophisticated organism engineering efforts are required to optimize production of high-value compounds from these networks. In principle, synthetic multi-gene transcriptional programs could be constructed to reengineer these networks for efficient industrial chemical production. In practice, however, our incomplete ability to understand and model the underlying networks, combined with our limited ability to predictably control the expression of multiple genes makes achieving this goal difficult. To overcome these challenges, we will combine new CRISPR-Cas multi-gene expression programs with computational modeling, machine learning, and multi-omics data to enhance the efficacy of design-build-test-learn (DBTL) cycles. For industrially promising microorganisms in early stages of development, creating technologies for rapidly engineering complex multi-gene programs could be transformative for accelerating data- and model-driven strain design. New CRISPR-Cas tools allow programmable gene activation (CRISPRa) or repression (CRISPRi) at multiple genes simultaneously, using the catalytically inactive Cas9 protein (dCas9) with guide RNAs that recognize DNA targets through predictable Watson-Crick base pairing. To enable accelerated DBTL cycles, we will combine these technologies with advanced Agile BioFoundry (ABF) capabilities for multi-omics data collection and machine learning. We will demonstrate the immediate applicability of these tools by rapidly improving the production of an industrial aromatic in multiple ABF organisms. We recently identified and optimized new transcriptional activators that can be linked to programmable CRISPR-Cas DNA binding domains to activate gene expression in E. coli. We can now use these CRISPRa tools as generalizable trans-acting regulators for combinatorial multi-gene expression tuning that can be easily transferred to new pathways and networks without additional genome engineering. We anticipate these tools will also transfer to new hosts. We have recently found that CRISPRa systems developed in E. coli can be readily ported to Pseudomonas putida, suggesting that multi-gene CRISPRa/i programs for diverse ABF organisms may be within reach.

59 BASIC BIOLOGICAL SCIENCES↗

Introducing Tropical Geometric Approaches to Delay Tolerant Networking Optimization

Delay Tolerant Networking (DTN) is the standard approach to the networking of space systems with the goal of supporting the Solar System Internet (SSI). Current space networks have a small scale and often depend on rigorously scheduled (pre-determined) contact opportunities; this manual approach inhibits scalability. The goal of this paper is to recast these scheduling problems in order to apply the optimization machinery of tropical geometry. Contact opportunities in space are dependent on such factors as orbital mechanics and asset availability, which induce time-varying connectivity; indeed, end-to-end connectivity might never occur. Routing optimization within this structure is classically difficult and typically utilizes Dijkstra's algorithm as applied to contact graphs. Alternatively, we follow the successes of tropical geometry in train schedule optimization, job assignments, and even traditional networking, by extending this approach to this more general (i.e. disconnected) problem space. These successes imply tropical geometry provides a useful framework in the context of DTNs, starting with applications to queuing theory and long-haul links. Recently, tropical geometry has been applied to parametric path optimization on graphs with variable edge weights. In this work, we extend these advances to account for the problem of routing in a space network, and find that tropical geometry is well-suited to the challenges offered by this new setting, including contact schedules featuring probabilities. Our approach leverages the combinatorial nature of the problem to give feasible shortest path trees in the presence of variable channel conditions and latency, evolving topologies, and uncertainty inherent in space routing. We discuss our tropical approach to DTN for two Python implementations, a Verilog Tropical ALU implementation, tropical frameworks for other parametric graph problems, and solution stability. Lastly, a program for future work is included to illuminate the path ahead.

Delay Tolerant Networking↗