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At least 937 records · Page 52

The PAH Emission Characteristics of the Reflection Nebula NGC 2023

We present 5–20 μ m spectral maps of the reflection nebula NGC 2023 obtained with the Infrared Spectrograph SL and SH modes on board the Spitzer Space Telescope, which reveal emission from polycyclic aromatic hydrocarbons (PAHs), C{sub 60}, and H{sub 2} superposed on a dust continuum. We show that several PAH emission bands correlate with each other and exhibit distinct spatial distributions that reveal a spatial sequence with distance from the illuminating star. We explore the distinct morphology of the 6.2, 7.7, and 8.6 μ m PAH bands and find that at least two spatially distinct components contribute to the 7–9 μ m PAH emission in NGC 2023. We report that the PAH features behave independently of the underlying plateaus. We present spectra of compact, oval PAHs ranging in size from C{sub 66} to C{sub 210}, determined computationally using density functional theory, and we investigate trends in the band positions and relative intensities as a function of PAH size, charge, and geometry. Based on the NASA Ames PAH database, we discuss the 7–9 μ m components in terms of band assignments and relative intensities. We assign the plateau emission to very small grains with possible contributions from PAH clusters and identify components in the 7–9 μ m emission that likely originate in these structures. Based on the assignments and the observed spatial sequence, we discuss the photochemical evolution of the interstellar PAH family as the PAHs are more and more exposed to the radiation field of the central star in the evaporative flows associated with the Photo-Dissociation Regions in NGC 2023.

79 ASTRONOMY AND ASTROPHYSICS↗

Spectroscopic Detection of a Stellar-like Photosphere in an Accreting Protostar

We present high-resolution (R is approximately equal to 18,000), high signal-to-noise 2 micron spectra of two luminous, X-ray flaring Class I protostars in the rho Ophiuchi cloud acquired with the NIRSPEC (near infrared spectrograph) of the Keck II telescope. We present the first spectrum of a highly veiled, strongly accreting protostar which shows photospheric absorption features and demonstrates the stellar nature of its central core. We find the spectrum of the luminous (L (sub bol) = 10 solar luminosity) protostellar source, YLW 15, to be stellar-like with numerous atomic and molecular absorption features, indicative of a K5 IV/V spectral type and a continuum veiling r(sub k) = 3.0. Its derived stellar luminosity (3 stellar luminosity) and stellar radius (3.1 solar radius) are consistent with those of a 0.5 solar mass pre-main-sequence star. However, 70% of its bolometric luminosity is due to mass accretion, whose rate we estimate to be 1.7 x 10(exp -6) solar masses yr(exp -1). We determine that excess infrared emission produced by the circumstellar accretion disk, the inner infalling envelope, and accretion shocks at the surface of the stellar core of YLW 15 all contribute significantly to its near-IR (infrared) continuum veiling. Its rotational velocity v sin i = 50 km s(exp -1) is comparable to those of flat-spectrum protostars but considerably higher than those of classical T Tauri stars in the rho Oph cloud. The protostar may be magnetically coupled to its circumstellar disk at a radius of 2 - 3 R(sub *). It is also plausible that this protostar can shed over half its angular momentum and evolve into a more slowly rotating classical T Tauri star by remaining coupled to its circumstellar disk (at increasing radius) as its accretion rate drops by an order of magnitude during the rapid transition between the Class I and Class II phases of evolution. The spectrum of WL 6 does not show any photospheric absorption features, and we estimate that its continuum veiling is r(sub k) is greater than or equal to 4.6. Its low luminosity (2 solar masses) and high veiling dictate that its central protostar is very low mass, M is approx. 0.1 solar masses. We also evaluate multi-epoch X ray data along with these spectra and conclude that the X ray variabilities of these sources are not directly related to their protostellar rotation velocities.

Greene, Thomas P.↗

Leveraging Natural Language Processing and Generative Models in Molecular Chemistry: Property Prediction and Novel Compound Generation

The accurate prediction of molecular properties is important for the rational design and the advancement of green chemistry and sustainable materials research. However, the predictive power of traditional computational chemistry methods is limited due to computational restrictions. Here, in this study, we examine an alternative approach to the accurate prediction of properties of organic compounds: natural language processing (NLP)-based molecular embedding. Using viscosity, partition coefficient (log P), and enthalpy of vaporization as test properties through a survey of comprehensive datasets comprising 5695 data points for viscosity, 25 870 data points for log P, and 2296 data points for enthalpy of vaporization. These are important properties for the design of greener, safer, and sustainable chemical processes. Models were trained using NLP methods such as Mol2vec and fine-tuned ChemBERTa, and results were compared with traditional input featurization techniques such as Morgan fingerprints and quantum chemistry derived sigma profiles and DFT features. Among the various machine learning models, Mol2vec demonstrated superior predictive capabilities, achieving the highest correlation coefficient (R 2 = 0.945) and lowest RMSE (0.106 mPa s) for viscosity, as well as high accuracy for log P and enthalpy of vaporization predictions. These findings establish the Mol2vec featurization technique, graph-convolutional neural networks (GCNN), and fine-tuned ChemBERTa model as powerful tools for predictive modeling of organic compounds properties, offering a significant improvement over previously used featurization techniques and opening up strategies for very-high-throughput computational screening. Finally, we integrated ML models with hybrid language-model-based generative adversarial networks (LM-GAN) to generate novel molecular sequences with desirable properties for different research applications. The ability to computationally design solvents with lower viscosity, lower log P, and lower enthalpy of vaporization offers a data-driven route to accelerating the discovery of sustainable alternatives to traditionally toxic solvents.

ChemBERTa↗

Mineral-Filled Fractures as Indicators of Multigenerational Fluid Flow in the Pahrump Hills Member of the Murray Formation, Gale Crater, Mars

Mineral-filled fractures (veins) are valuable indicators of deformation and fluid flow within a sedimentary package. Information obtained from vein morphology, texture, and chemistry may elucidate the sequence and nature of postdepositional fluid events. Additional information from vein patterns and interactions between veins and host rock provides insight into fracture formation mechanism(s). The widespread occurrence of veins and other diagenetic features in the sedimentary rock record preserved in Gale crater, Mars, indicates that postdepositional fluids were regionally active considerably later in time than the primary fluviolacustrine environments responsible for the deposition of Mount Sharp strata. Here we report a suite of veins within the Murray formation at the Pahrump Hills locality that were investigated using the scientific payload of the Mars Science Laboratory Curiosity rover. Based on an analysis of vein color, morphology, and texture, and corroborated by vein chemistry, we interpret three distinct vein types at Pahrump Hills: gray veins, white veins, and dark-toned veins. These veins represent distinct, separate episodes of postdepositional fluid flow, suggesting a protracted history of fluid stability in Gale crater. Additionally, we utilize vein patterns across multiple lithologies at the Pahrump Hills field site to suggest hydrofracture as the primary mechanism of fracture formation.

58 GEOSCIENCES↗

The Enterprise , a massive transposon carrying Spok meiotic drive genes

The genomes of eukaryotes are full of parasitic sequences known as transposable elements (TEs). Here, we report the discovery of a putative giant tyrosine-recombinase-mobilized DNA transposon, Enterprise, from the model fungus Podospora anserina. Previously, we described a large genomic feature called the Spok block which is notable due to the presence of meiotic drive genes of the Spok gene family. The Spok block ranges from 110 kb to 247 kb and can be present in at least four different genomic locations within P. anserina, despite what is an otherwise highly conserved genome structure. We propose that the reason for its varying positions is that the Spok block is not only capable of meiotic drive but is also capable of transposition. More precisely, the Spok block represents a unique case where the Enterprise has captured the Spoks, thereby parasitizing a resident genomic parasite to become a genomic hyperparasite. Furthermore, we demonstrate that Enterprise (without the Spoks) is found in other fungal lineages, where it can be as large as 70 kb. Lastly, we provide experimental evidence that the Spok block is deleterious, with detrimental effects on spore production in strains which carry it. This union of meiotic drivers and a transposon has created a selfish element of impressive size in Podospora, challenging our perception of how TEs influence genome evolution and broadening the horizons in terms of what the upper limit of transposition may be.

59 BASIC BIOLOGICAL SCIENCES↗

Earth microbial co-occurrence network reveals interconnection pattern across microbiomes

Microbial interactions shape the structure and function of microbial communities; microbial association networks in specic environments have been widely developed to explore these complex systems, but their wired pattern across microbiomes in various environments at the global scale remains unexplored. Here we have inferred an Earth microbial association network from a communal catalogue with 23,595 samples and 12,646 exact sequence variants from 14 environments in the Earth Microbiome Project dataset. Results: This non-random scale-free Earth microbial association network consisted of 8 taxonomy distinct modules linked with dierent environments, which featured environment specic microbial associations. Dierent topological features of subnetworks inferred from datasets trimmed into uniform size indicate distinct association patterns in the microbiomes of various environments. The proportions of specialist edges, which ranged from 43.0% to 65.7%, highlight that environmental specic microbial associations are essential features of microbiomes in various environments. Based on edge-overlap similarity, the microbiomes of various environments were clustered into two groups, which were mainly bridged by the microbiomes of plant and animal surface. Acidobacteria Gp2 and Nisaea were identied as hubs in most of subnetworks. Negative edges proportions ranged from 1.9% in the soil subnetwork to 48.9% the non-saline surface subnetwork, suggesting various environments experience distinct intensities of competition or niche dierentiation. Conclusion: This investigation provides a new resource for examining Earth microbial association patterns across environments and emphasizes the network perspective for comprehensively understanding unique microbiome features. Keywords: Association pattern; Earth microbiomes; Genelist edges; Network hubs; Negative associations; Specialist edges; Topological properties

59 BASIC BIOLOGICAL SCIENCES↗

A new infrared complex and molecular cloud in Orion

A new complex of infrared sources about one minute across has been discovered in Orion. Associated with this cluster is an extended molecular cloud producing intense CO emission. Its measured infrared luminosity is less than 500 times the luminosity of the sun. The complex is hypothesized to be composed of pre-main-sequence sources. Several arguments suggest that these stars heat dust in the larger molecular cloud and thus cause the strong CO emission. The complex is not associated with a known visible or radio continuum feature, and searches over a 0.7 x 0.7-deg field show no other comparable unknown 2.2-micron sources.

Gatley, I.↗

Desert pavement study at Amboy, California

Desert pavement is a general term describing a surface that typically consists of a thin layer of cm-sized rock fragments set on top of a layer of finer material in which no fragments are found. An understanding of desert pavement is important to planetary geology because they may play a major role in the formation and visibility of various aeolian features such as wind streaks, which are important on Mars and may be important on Venus. A field study was conducted in Amboy, California to determine the formation mechanism of desert pavements. The probable sequence of events for the formation and evolution of a typical desert pavement surface, based on this experiment and the work of others, is as follows. Starting with a layer of surface material consisting of both fine particles and rock fragments, aeolian deflation will rapidly erode the surface until an armored lag is developed, after which aeolian processes become less important. The concentration of fragments then slowly increases as new fragments are brought to the surface from the subsurface and as fragments move downslope by sheet wash. Sheet wash would be responsible for removing very fine particles from the surface and for moving the fragments relative to one another, forming interlocks.

Williams, S.↗

The Pongola structure of southeastern Africa - The world's oldest preserved rift?

Rocks of the Pongola Supergroup form an elongate belt in the Archean Kaapvaal Craton of southern Africa. Because these rocks exhibit many features that are characteristic of rocks deposited in continental rifts, including rapid lateral variations in thickness and character of sediments, volcanic rocks that are bimodal in silica content, coarse, basement derived conglomerates and thick sequences of shallow water sedimentary facies associations, it is suggested that the Pongola Supergroup was deposited in such a rift. The age of these rocks (approximately 3.0 Ga) makes the Pongola structure the world's oldest well-preserved rift so far recognized, and comparison of the Pongola Rift with other rifts formed more recently in earth history reveals striking similarities, suggesting that the processes that formed this rift were not significantly different from those that form continental rifts today.

Burke, K.↗

Implementing Distributed Operations: A Comparison of Two Deep Space Missions

Two very different deep space exploration missions--Mars Exploration Rover and Cassini--have made use of distributed operations for their science teams. In the case of MER, the distributed operations capability was implemented only after the prime mission was completed, as the rovers continued to operate well in excess of their expected mission lifetimes; Cassini, designed for a mission of more than ten years, had planned for distributed operations from its inception. The rapid command turnaround timeline of MER, as well as many of the operations features implemented to support it, have proven to be conducive to distributed operations. These features include: a single science team leader during the tactical operations timeline, highly integrated science and engineering teams, processes and file structures designed to permit multiple team members to work in parallel to deliver sequencing products, web-based spacecraft status and planning reports for team-wide access, and near-elimination of paper products from the operations process. Additionally, MER has benefited from the initial co-location of its entire operations team, and from having a single Principal Investigator, while Cassini operations have had to reconcile multiple science teams distributed from before launch. Cassini has faced greater challenges in implementing effective distributed operations. Because extensive early planning is required to capture science opportunities on its tour and because sequence development takes significantly longer than sequence execution, multiple teams are contributing to multiple sequences concurrently. The complexity of integrating inputs from multiple teams is exacerbated by spacecraft operability issues and resource contention among the teams, each of which has their own Principal Investigator. Finally, much of the technology that MER has exploited to facilitate distributed operations was not available when the Cassini ground system was designed, although later adoption of web-based and telecommunication tools has been critical to the success of Cassini operations.

Cassini Mission↗

Combined Space Environmental Exposure Tests of Multi-Junction GaAs/Ge Solar Array Coupons

A set of multi-junction GaAs/Ge solar array test coupons were subjected to a sequence of 5-year increments of combined environmental exposure tests. The purpose of this test program is to understand the changes and degradation of the solar array panel components, including its ESD mitigation design features in their integrated form, after multiple years (up to 15) of simulated geosynchronous space environment. These tests consist of: UV radiation, electrostatic discharge (ESD), electron/proton particle radiation, thermal cycling, and ion thruster plume exposures. The solar radiation was produced using a Mercury-Xenon lamp with wavelengths in the UV spectrum ranging from 230 to 400 nm. The ESD test was performed in the inverted-gradient mode using a low-energy electron (2.6 - 6 keV) beam exposure. The ESD test also included a simulated panel coverglass flashover for the primary arc event. The electron/proton radiation exposure included both 1.0 MeV and 100 keV electron beams simultaneous with a 40 keV proton beam. The thermal cycling included simulated transient earth eclipse for satellites in geosynchronous orbit. With the increasing use of ion thruster engines on many satellites, the combined environmental test also included ion thruster exposure to determine whether solar array surface erosion had any impact on its performance. Before and after each increment of environmental exposures, the coupons underwent visual inspection under high power magnification and electrical tests that included characterization by LAPSS, Dark I-V, and electroluminescence. This paper discusses the test objective, test methodologies, and preliminary results after 5 years of simulated exposure.

Hoang, Bao↗

Implementing distributed operations : a comparison of two Deep Space Missions

Two very different deep space exploration missions—Mars Exploration Rover and Cassini—have made use of distributed operations for their science teams. In the case of MER, the distributed operations capability was implemented only after the prime mission was completed, as the rovers continued to operate well in excess of their expected mission lifetimes; Cassini, designed for a prime mission of four years, had planned for distributed operations from its inception. The rapid command turnaround timeline of MER, as well as many of the operations features implemented to support it, have proven to be conducive to distributed operations. These features include: a single science team leader during the tactical operations timeline, highly integrated science and engineering teams, processes and file structures designed to permit multiple team members to work in parallel to deliver sequencing products, web-based spacecraft status and planning reports for team-wide access, and near-elimination of paper products from the operations process.

Larsen, Barbara↗

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗

The faintest stars - The luminosity and mass functions at the bottom of the main sequence

We present IR K-band photometry of complete samples of VLM candidates constructed from IIIaF and IVN plates in 10 fields taken as part of the POSSII and UKSRC surveys. Using the I-K colors constructed for these stars we estimate a bolometric luminosity function which extends to M(Bol) = 13.75. We find significant evidence for a luminosity function decreasing toward these luminosities. We also find that our results are consistent with those of studies based on the Nearby Star sample, when those data are presented as a bolometric luminosity function. We convert our observed luminosity function into a mass function, which extends with reasonable statistics to 0.08 solar masses - the H-burning minimum mass. We find significant evidence for features in the mass function at these masses. Specifically, the mass function 'turns over' at 0.25 solar mass, goes through a local minimum at about 0.15 solar mass, and may increase again below 0.1 solar mass - none of these features are predicted by any of the current theories of star formation. Lastly, the mass density we observe just above the H-burning minimum mass makes it difficult to envisage brown dwarfs contributing significant quantities of missing mass without invoking either a mass function in this region significantly steeper than that seen for main-sequence stars, or an extremely low cutoff mass to the mass function.

Tinney, Christopher G.↗

Description of the PMAD DC test bed architecture and integration sequence

NASA-Lewis is responsible for the development, fabrication, and assembly of the electric power system (EPS) for the Space Station Freedom (SSF). The SSF power system is radically different from previous spacecraft power systems in both the size and complexity of the system. Unlike past spacecraft power system the SSF EPS will grow and be maintained on orbit and must be flexible to meet changing user power needs. The SSF power system is also unique in comparison with terrestrial power systems because it is dominated by power electronic converters which regulate and control the power. Although spacecraft historically have used power converters for regulation they typically involved only a single series regulating element. The SSF EPS involves multiple regulating elements, two or more in series, prior to the load. These unique system features required the construction of a testbed which would allow the development of spacecraft power system technology. A description is provided of the Power Management and Distribution (PMAD) DC Testbed which was assembled to support the design and early evaluation of the SSF EPS. A description of the integration process used in the assembly sequence is also given along with a description of the support facility.

Beach, R. F.↗

Evidence of a Discontinuous Disk Structure Around the Herbig Ae Star HD 139614

The formation and evolution of a planetary system are intrinsically linked to the evolution of the primordial accretion disk and its dust and gas content. A new class of pre-main sequence objects has been recently identified as pre-transitional disks. They present near-infrared excess coupled to a flux deficit at about 10 microns and a rising mid-infrared and far-infrared spectrum. These features suggest a disk structure with inner and outer dust components, separated by a dust-depleted region (or gap). This could be the result of particular planet formation mechanisms that occur during the disk evolution. We here report on the first interferometric observations of the disk around the Herbig Ae star HD 139614. Its infrared spectrum suggests a flared disk, and presents pre-transitional features, namely a substantial near-infrared excess accompanied by a dip around 6 microns and a rising mid-infrared part. In this framework, we performed a study of the spectral energy distribution (SED) and the mid-infrared VLTI/MIDI interferometric data to constrain the spatial structure of the inner dust disk region and assess its possibly multi-component structure. We based our work on a temperature-gradient disk model that includes dust opacity.While we could not reproduce the SED and interferometric visibilities with a one-component disk, a better agreement was obtained with a two-component disk model composed of an optically thin inner disk extending from 0.22 to 2.3 AU, a gap, and an outer temperature-gradient disk starting at 5.6 AU. Therefore, our modeling favors an extended and optically thin inner dust component and in principle rules out the possibility that the near-infrared excess originates only from a spatially confined region. Moreover, the outer disk is characterized by a very steep temperature profile and a temperature higher than 300 K at its inner edge. This suggests the existence of a warm component corresponding to a scenario where the inner edge of the outer disk is directly illuminated by the central star. This is an expected consequence of the presence of a gap, thus indicative of a "pre-transitional" structure.

primordial accretion disk↗