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At least 703 records · Page 39

Resistance exercise countermeasures for space flight: implications of training specificity

While resistance exercise should be a logical choice for prevention of strength loss during unloading, the principle of training specificity cannot be overlooked. Our purpose was to explore training specificity in describing the effect of our constant load exercise countermeasure on isokinetic strength performance. Twelve healthy men (mean +/- SD: 28.0 +/- 5.2 years, 179.4 +/- 3.9 cm, 77.5 +/- 13.6 kg) were randomly assigned to no exercise or resistance exercise (REX) during 14 days of bed rest. REX performed five sets of leg press exercise to volitional fatigue (6-10 repetitions) every other day. Unilateral isokinetic concentric-eccentric knee extension testing performed before and on day 15 prior to reambulation included torque-velocity and power-velocity relationships at four velocities (0.52, 1.75, 2.97, and 4.19 rad s-1), torque-position relationship, and contractile work capacity (10 repetitions at 1.05 rad s-1). Two (group) x 2 (time) ANOVA revealed no group x time interactions; thus, groups were combined. Across velocities, angle-specific torque fell 18% and average power fell 20% (p < 0.05). No velocity x time or mode (concentric/eccentric) x time interactions were noted. Torque x position decreased on average 24% (p < 0.05). Total contractile work dropped 27% (p < 0.05). Results indicate bed rest induces rapid and marked reductions in strength and our constant load resistance training protocol did not prevent isokinetic strength losses. Differences between closed-chain training and open-chain testing may explain the lack of protection.

Randomized Controlled Trial↗

YPK9 and WHI2 Negatively Interact during Oxidative Stress

Yeast PARK9 (YPK9) shares homology with human ATP13A2, which encodes a polyamine transporter implicated in juvenile forms of Parkinson’s disease. We used YPK9 to gain insight into how ATP13A2 affects cell growth and sensitivity to oxidative stress. Surprisingly, the YPK9 deletion strain from the Saccharomyces cerevisiae deletion collection (YKO) in wildtype BY4741 (mating type a) grew faster and was more resistant to hydrogen peroxide than a commercial, putative parental BY4741 wildtype strain (BY4741 COM ). In contrast, deleting YPK9 from BY4741 COM rendered it very sensitive to hydrogen peroxide, suggesting its background is different from that of the deletion collection. Whole-genome sequencing revealed that BY4741 COM and BY4741 COM ypk9Δ contain a novel premature stop codon near the 3' end of WHI2 (WHI2 G1324T ), whereas the collection’s YPK9 deletion strain contains WHI2, which encodes a 486 amino acid protein, Whi2p. Replacing full-length WHI2 with the sequence coding for the predicted truncation (Whi2p E442* ) rendered strains more sensitive to hydrogen peroxide, whereas the converse replacement rendered them more resistant. The sequences of WHI2 in 20 randomly chosen strains from the collection encode the full-length protein, indicating that the putative parental BY4741 WHI2 G1324T strain’s genetic background differs from that of the deletion collection. Examination of WHI2 sequences in several commonly used wildtype S. cerevisiae strains and isolates revealed other Whi2p truncations that might yield altered phenotypes. Together, these results demonstrate a novel premature stop codon in WHI2 that renders yeast sensitive to hydrogen peroxide; they also reveal a negative genetic interaction between WHI2 and YPK9 in the presence of hydrogen peroxide in the BY4741 background.

59 BASIC BIOLOGICAL SCIENCES↗

The crystal structure of dGTPase reveals the molecular basis of dGTP selectivity

Deoxynucleotide triphosphohydrolases (dNTPases) play a critical role in cellular survival and DNA replication through the proper maintenance of cellular dNTP pools. While the vast majority of these enzymes display broad activity toward canonical dNTPs, such as the dNTPase SAMHD1 that blocks reverse transcription of retroviruses in macrophages by maintaining dNTP pools at low levels, Escherichia coli ( Ec )-dGTPase is the only known enzyme that specifically hydrolyzes dGTP. However, the mechanism behind dGTP selectivity is unclear. Here we present the free-, ligand (dGTP)- and inhibitor (GTP)-bound structures of hexameric Ec- dGTPase, including an X-ray free-electron laser structure of the free Ec- -dGTPase enzyme to 3.2 Å. To obtain this structure, we developed a method that applied UV-fluorescence microscopy, video analysis, and highly automated goniometer-based instrumentation to map and rapidly position individual crystals randomly located on fixed target holders, resulting in the highest indexing rates observed for a serial femtosecond crystallography experiment. Our structures show a highly dynamic active site where conformational changes are coupled to substrate (dGTP), but not inhibitor binding, since GTP locks dGTPase in its apo- form. Moreover, despite no sequence homology, Ec- dGTPase and SAMHD1 share similar active-site and HD motif architectures; however, Ec- dGTPase residues at the end of the substrate-binding pocket mimic Watson–Crick interactions providing guanine base specificity, while a 7-Å cleft separates SAMHD1 residues from dNTP bases, abolishing nucleotide-type discrimination. Furthermore, the structures shed light on the mechanism by which long distance binding (25 Å) of single-stranded DNA in an allosteric site primes the active site by conformationally “opening” a tyrosine gate allowing enhanced substrate binding.

Barnes, Christopher O.↗

Coupling between collective modes in the deformed 98 Zr nucleus: Insights from consistent HFB + QRPA calculations with the Gogny interaction

The zirconium isotopes exhibit structural properties that present multiple challenges to nuclear theory. Investigations of the coupling present within isoscalar modes and within isovector modes are scarce but important for advancing our understanding of the microscopic picture of nuclei. To explore some of these underlying coupling features, and to test the predictive power of a state-of-the-art nuclear structure approach, we provide a detailed analysis of the properties of 90,96,98 Zr . This region includes a benchmarking case and offers insights into nuclear deformation phenomena. Here, to investigate the coupling between collective modes in deformed nuclei, we focused our analysis on the ground and excited-state properties of these isotopes, employing a consistent approach with the axially symmetric deformed Hartree-Fock-Bogoliubov (HFB) and the quasiparticle random phase approximation (QRPA) framework, both using the Gogny D1M force. This approach effectively describes both low-lying and giant-resonance states. We devoted special attention to the deformed 98 Zr nucleus, where we confirm the existence of coupling between monopole and quadrupole excitations through the 𝐾 𝜋 = 0 + QRPA components and demonstrate an analogous dipole-octupole coupling through the 𝐾 𝜋 = 0 − and 𝐾 𝜋 = 1 − components. Intrinsic transition densities and associated radial projections illustrate the coupling. Our work complements and extends earlier studies carried out using density-functional-based methods and notably, we included the complete Coulomb interaction also in the pairing fields, i.e., we treat terms exactly that are approximated in typical calculations that use the Gogny D1 and D2 interaction families.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Coupling between collective modes in the deformed 98 Zr nucleus: Insights from consistent HFB+QRPA calculations with the Gogny interaction

The Zirconium isotopes exhibit structural properties that present multiple challenges to nuclear theory. Investigations of the coupling present within isoscalar modes and within isovector modes are scarce but important for advancing our understanding of the microscopic picture of nuclei. To explore some of these underlying coupling features, and to test the predictive power of a state-of the-art nuclear structure approach, we provide a detailed analysis of the properties of 90,96,98Zr. This region includes a benchmarking case and offers insights into nuclear deformation phenomena. To investigate the coupling between collective modes in deformed nuclei, we focused our analysis on the ground and excited-state properties of these isotopes, employing a consistent approach with the axially-symmetric deformed Hartree-Fock-Bogoliubov (HFB) and the Quasiparticle Random Phase Approximation (QRPA) framework, both using the Gogny D1M force. This approach effectively describes both low-lying and giant-resonance states. We devoted special attention to the deformed 98Zr nucleus, where we confirm the existence of coupling between monopole and quadrupole excitations through the K π = 0 + QRPA components and demonstrate an analogous dipole-octupole coupling through the K π = 0 − and K π = 1 − components. Intrinsic transition densities and associ ated radial projections illustrate the coupling. Our work complements and extends earlier studies carried out using density-functional-based methods and notably, we included the complete Coulomb interaction also in the pairing fields, i.e. we treat terms exactly that are approximated in typical calculations that use the Gogny D1 and D2 interaction families.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Tunable Stochasticity in an Artificial Spin Network

Metamaterials present the possibility of artificially generating advanced functionalities through engineering of their internal structure. Artificial spin networks, in which a large number of nanoscale magnetic elements are coupled together, are promising metamaterial candidates that enable the control of collective magnetic behavior through tuning of the local interaction between elements. In this work, the motion of magnetic domain-walls in an artificial spin network leads to a tunable stochastic response of the metamaterial, which can be tailored through an external magnetic field and local lattice modifications. This type of tunable stochastic network produces a controllable random response exploiting intrinsic stochasticity within magnetic domain-wall motion at the nanoscale. An iconic demonstration used to illustrate the control of randomness is the Galton board. In this system, multiple balls fall into an array of pegs to generate a bell-shaped curve that can be modified via the array spacing or the tilt of the board. A nanoscale recreation of this experiment using an artificial spin network is employed to demonstrate tunable stochasticity. Furthermore, this type of tunable stochastic network opens new paths toward post-Von Neumann computing architectures such as Bayesian sensing or random neural networks, in which stochasticity is harnessed to efficiently perform complex computational tasks.

Artificial spin network↗

Structure, Magnetism, and First-Principles Modeling of the Na 0.5 La 0.5 RuO 3 Perovskite

High-purity polycrystalline Na 0.5 La 0.5 RuO 3 was synthesized by a solid-state method, and its properties were studied by magnetic susceptibility, heat capacity, and resistivity measurements. In this study, we find it to be an orthorhombic perovskite, in contrast to an earlier report, with random La/Na mixing. With a Curie–Weiss temperature of –231 K and an effective moment of 2.74 μB/mol Ru, there is no magnetic ordering down to 1.8 K. A broad hump at 1.4 K in the heat capacity, however, indicates the presence of a glassy magnetic transition, which we attribute to the influence of the random distribution of Na and La on the perovskite A-sites. Comparison to CaRuO 3 , a structurally ordered ruthenate perovskite with a similar A-site ionic radius and magnetic properties, is presented. First-principles calculations indicate that the Na–La distribution determines the local magnetic exchange interactions between Ru ions, favoring either antiferromagnetic or ferromagnetic coupling when the local environment is Na- or La-rich. Thus, our data and analysis suggest that mixing cations with different charges and sizes on the A-site in this perovskite results in magnetic frustration through a balance of local magnetic exchange interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A Multifunctional Cosolvent Pair Reveals Molecular Principles of Biomass Deconstruction

The complex structure of plant cell walls resists chemical or biological degradation, challenging the breakdown of lignocellulosic biomass into renewable chemical precursors that could form the basis of future production of green chemicals and transportation fuels. In this report, experimental and computational results reveal that the effect of the tetrahydrofuran (THF)–water cosolvents on the structure of lignin and on its interactions with cellulose in the cell wall drives multiple synergistic mechanisms leading to the efficient breakdown and fractionation of biomass into valuable chemical precursors. Molecular simulations reflect that THF–water is an excellent “theta” solvent, such that lignin dissociates from itself and from cellulose and expands to form a random coil. The expansion of the lignin molecules exposes interunit linkages, rendering them more susceptible to depolymerization by acid-catalyzed cleavage of aryl-ether bonds. Nanoscale infrared sensors confirm cosolvent-mediated molecular rearrangement of lignin in the cell wall of micrometer-thick hardwood slices and track the disappearance of lignin. At bulk scale, adding dilute acid to the cosolvent mixture liberates the majority of the hemicellulose and lignin from biomass, allowing unfettered access of cellulolytic enzymes to the remaining cellulose-rich material, allowing them to sustain high rates of hydrolysis to glucose without enzyme deactivation. Through this multiscale analysis, synergistic mechanisms for biomass deconstruction are confirmed, portending a paradigm shift toward first-principles design and evaluation of other cosolvent methods to realize low cost fuels and bioproducts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Nonresonant interaction of heavy ions with electromagnetic ion cyclotron waves

The motion of a heavy ion in the presence of an intense ultralow-frequency electromagnetic wave propagating along the dc magnetic field is analyzed. Starting from the basic equations of motion and from their associated two invariants, the heavy ion velocity-space trajectories are drawn. It is shown that after a certain time, particles whose initial phase angles are randomly distributed tend to bunch together, provided that the wave intensity b-sub-1 is sufficiently large. The importance of these results for the interpretation of the recently observed acceleration of singly charged He ions in conjunction with the occurrence of large-amplitude ion cyclotron waves in the equatorial magnetosphere is discussed.

Berchem, J.↗

Venus - Dead or alive?

In situ nightside electric field observations from the Pioneer Venus Orbiter have been interpreted as evidence of extensive lightning in the lower atmosphere of Venus. The scenario, including proposed evidence of clustering of lightning over surface highland regions, has encouraged the acceptance of currently active volcanic output as part of several investigations of the dynamics and chemistry of the atmosphere and the geology of the planet. However, the correlation between the 100-hertz electric field events attributed to lightning and nightside ionization troughs resulting from the interaction of the solar wind with the ionosphere indicates that the noise results from locally generated plasma instabilities and not from any behavior of the lower atmosphere. Furthemore, analysis of the spatial distribution of the noise shows that it is not clustered over highland topography, but rather occurs at random throughout the latitude and longitude regions sampled by the orbiter during the first 5 years of operation, from 1978 to 1984. Thus the electric field observations do not identify lightning and do not provide a basis for inferring the presence of currently active volcanic output. In the absence of known evidence to the contrary, it appears that Venus is no longer active.

Taylor, Harry A., Jr.↗

VLF noises triggered by whistlers as observed in the topside ionosphere

ISIS-1, ISIS-2, and DE-1 VLF electric field data from Kashima station, Japan are used to investigate characteristics of whistler triggered emissions in the topside ionosphere. The occurrence rate for nonducted whistler triggered emission is shown to be distributed randomly between L = 2.0 and 4.2, while the occurrence rate of ducted whistler triggered emissions increases with latitudes between L = 1.5 and 2.9, attains a maximum at L = 2.7, and abruptly drops off at L = 3.0. The findings indicate that some whistler triggered emissions may be generated by the interaction of whistlers with magnetospheric electrons.

Nakamura, Yoshikatsu↗

The response of an airplane to random atmospheric disturbances

The statistical approach to the gust-load problem which consists in considering flight through turbulent air to be a stationary random process is extended by including the effect of lateral variation of the instantaneous gust intensity on the aerodynamic forces. The forces obtained in this manner are used in dynamic analyses of rigid and flexible airplanes free to move vertically, in pitch, and in roll. The effect of the interaction of longitudinal, vertical, and lateral gusts on the wing stresses is also considered.

LOADS, GUST - WINGS↗

The response of an airplane to random atmospheric disturbances

The statistical approach to the gust-load problem, which consists in considering flight through turbulent air to be a stationary random process, is extended by including the effect of lateral variation of the instantaneous gust intensity on the aerodynamic forces. The forces obtained in this manner are used in dynamic analyses of rigid and flexible airplanes free to move vertically, in pitch, and in roll. The effect of the interaction of longitudinal, normal, and lateral gusts on the wind stresses is also considered.

Diederich, Franklin W↗

What’s That Supposed to Mean? Capturing Micro-Behaviors in Teams

Future long-duration space exploration (LDSE) crews will require extensive coordination, cooperation, and team functioning as they face a myriad of challenges rooted in both taskwork and teamwork (Bell et al., 2015; Landon et al., 2018). While exposed to extreme conditions, crew members must navigate living and working together in prolonged confinement. Moreover, astronaut teams are becoming increasingly diverse, introducing significant variability in team composition. This increasing diversity, alongside traditional constraints of LDSE, introduces additional challenges into effective team functioning. To date, most methods for capturing team functioning rely on self-report measures. Such measures are prone to several limitations, including but not limited to social desirability bias, halo effect, and leniency effects (Trull & Ebner-Priemer, 2013), which skew data and limit nuanced understandings of phenomena at play. Self-report measures broadly capture team functioning, lending the nature of such methods to identifying underlying “macro”-behaviors (i.e., behaviors that are long-standing and last over time). However, team functioning is far more complex than a series of macro-behaviors, rendering reliance on self-report data deficient for accurate measurement. Recent research demonstrates the potential of alternative methods for capturing team functioning, such as speech and physiological data (Chaffin et al., 2017; Murray & Oertel, 2018). Consequently, these methods are more suitable for capturing micro-behaviors: brief, often unconscious expressions that affect the extent to which an individual feels included by others around them (Paletz et al., 2013). Micro-behaviors can be further classified into microaggressions (i.e., subtle, negative exchanges; Keller & Galgay, 2010) or micro-affirmations (i.e., subtle, positive exchanges; Kyte et al. 2020), both of which influence team functioning. Due to the subtle nature of micro-behaviors, contextual factors have a significant impact when determining if it is aggressive or affirmative. Additionally, several iterations of microbehaviors can have lingering effects on team interactions. For example, the use of “mm-hmm” by a crew member can function as both a micro-affirmation and micro-aggression. Specifically, it can be indication of active listening (i.e., micro-affirmation) or as an expression of annoyance (i.e., aggression) depending on the context in which it occurs. Auditory features (e.g., tone, frequency) can help delineate between the two forms; however, the contextual factors (e.g., previous interactions between team members, crew demographics) add a layer of complexity that render auditory features alone as insufficient to capture micro-behaviors. Consequently, this paper seeks to provide a novel approach in which multi-modal data (i.e., auditory features and contextual features) are used in a random-forest model to better identify distinguishing characteristics between micro-affirmations and micro-aggressions. In turn, detected micro-behaviors are used to predict team performance, thereby demonstrating the value of capturing micro-behaviors as supplemental data to macro-behaviors.

Sydney R. Begerowski↗

What’s That Supposed to Mean? Capturing Micro-Behaviors in Teams

Future long-duration space exploration (LDSE) crews will require extensive coordination, cooperation, and team functioning as they face a myriad of challenges rooted in both taskwork and teamwork (Bell et al., 2015; Landon et al., 2018). While exposed to extreme conditions, crew members must navigate living and working together in prolonged confinement. Moreover, astronaut teams are becoming increasingly diverse, introducing significant variability in team composition. This increasing diversity, alongside traditional constraints of LDSE, introduces additional challenges into effective team functioning. To date, most methods for capturing team functioning rely on self-report measures. Such measures are prone to several limitations, including but not limited to social desirability bias, halo effect, and leniency effects (Trull & Ebner-Priemer, 2013), which skew data and limit nuanced understandings of phenomena at play. Self-report measures broadly capture team functioning, lending the nature of such methods to identifying underlying “macro”-behaviors (i.e., behaviors that are long-standing and last over time). However, team functioning is far more complex than a series of macro-behaviors, rendering reliance on self-report data deficient for accurate measurement. Recent research demonstrates the potential of alternative methods for capturing team functioning, such as speech and physiological data (Chaffin et al., 2017; Murray & Oertel, 2018). Consequently, these methods are more suitable for capturing micro-behaviors: brief, often unconscious expressions that affect the extent to which an individual feels included by others around them (Paletz et al., 2013). Micro-behaviors can be further classified into micro-aggressions (i.e., subtle, negative exchanges; Keller & Galgay, 2010) or micro-affirmations (i.e., subtle, positive exchanges; Kyte et al. 2020), both of which influence team functioning. Due to the subtle nature of micro-behaviors, contextual factors have a significant impact when determining if it is aggressive or affirmative. Additionally, several iterations of micro-behaviors can have lingering effects on team interactions. For example, the use of “mm-hmm” by a crew member can function as both a micro-affirmation and micro-aggression. Specifically, it can be indication of active listening (i.e., micro-affirmation) or as an expression of annoyance (i.e., aggression) depending on the context in which it occurs. Auditory features (e.g., tone, frequency) can help delineate between the two forms; however, the contextual factors (e.g., previous interactions between team members, crew demographics) add a layer of complexity that render auditory features alone as insufficient to capture micro-behaviors. Consequently, this paper seeks to provide a novel approach in which multi-modal data (i.e., auditory features and contextual features) are used in a random-forest model to better identify distinguishing characteristics between micro-affirmations and micro-aggressions. In turn, detected micro-behaviors are used to predict team performance, thereby demonstrating the value of capturing micro-behaviors as supplemental data to macro-behaviors.

Sydney Begerowski↗

In‐operando crystallization study of zinc nitrate hexahydrate using zinc oxide nucleators

Abstract Supercooling (∆T Sup ) is one of the crucial problems in utilizing a phase change material (PCM), which has been attempted to overcome using nucleators possessing small lattice disregistry without comprehensive understanding of the nucleation phenomenon. Here, this work studies the interactions between nucleators and PCM via in‐operando direct visualization for the first time, to the best of our knowledge, to better understand the crystallization process during freezing of a PCM using zinc nitrate hexahydrate (ZNH) and a zinc oxide (ZnO) nucleator. According to our in‐operando study, freezing was randomly initiated by only a fraction of the nucleator particles during each thermal cycling. However, previously unresponsive nucleators also suddenly crystalized PCM when the propagating crystal encountered them, improving the crystal initiation and thereby ∆T Sup . Consequently, for equal wt% of ZnO, better nucleation behavior was obtained using uniformly distributed small nucleator particles throughout the PCM, as compared to either large or poorly distributed aggregated nucleators. DSC results using ZnO‐needle as nucleator confirmed a 38% and 33.3% lower ∆T Sup upon employing 5 wt% small (individual) particles (~4 μm on average), as compared to using equal wt% of small (aggregated) particle clusters (~200 μm) and large particles (~46 μm), respectively. Crystallization of ZNH caused additional hair‐like ZnO growth preferentially decorated along lateral faces of the original nucleator particles. The in‐operando studies are valuable tools to correlate inherent crystallization phenomena to the practical thermal energy storage properties of the system.

Chakraborty, Anirban↗

The influence of preexisting host rock damage on fault network localization

The transition from stable to unstable fracture propagation occurs when fractures begin to interact and link. Thus, fracture network coalescence controls how rocks and engineered structures fail. Here, to constrain the factors that influence localization in shear zones under brittle conditions, we build discrete element method models with a rough fault embedded in a shear zone. We add varying numbers of diffuse, randomly-placed weaknesses to examine the influence of diffuse damage on fracture network localization. The number of weaknesses controls the localization behavior of the fault network and the final fault geometry. We quantify localization using the Gini coefficient of the fracture volume, which measures the nonuniformity in a population. Each model generally increases in localization toward failure. However, models with more diffuse damage experience delocalization phases that are superimposed on the overall trend of increasing localization. The observed link between delocalization and host rock damage may help explain the varying localization of low magnitude seismicity in southern California. Models with more diffuse damage produce more complex fault geometries comprised of several parallel strands of wing cracks. The propagation of these wing cracks reduces the shear stress acting on the model boundaries, indicating that this fracture development increases the mechanical efficiency of the system.

58 GEOSCIENCES↗

Stochastic minibatch approach to the ptychographic iterative engine

The ptychographic iterative engine (PIE) is a widely used algorithm that enables phase retrieval at nanometer-scale resolution over a wide range of imaging experiment configurations. By analyzing diffraction intensities from multiple scanning locations where a probing wavefield interacts with a sample, the algorithm solves a difficult optimization problem with constraints derived from the experimental geometry as well as sample properties. The effectiveness at which this optimization problem is solved is highly dependent on the ordering in which we use the measured diffraction intensities in the algorithm, and random ordering is widely used due to the limited ability to escape from stagnation in poor-quality local solutions. In this study, we introduce an extension to the PIE algorithm that uses ideas popularized in recent machine learning training methods, in this case minibatch stochastic gradient descent. Our results demonstrate that these new techniques significantly improve the convergence properties of the PIE numerical optimization problem.

47 OTHER INSTRUMENTATION↗