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At least 1,333 records · Page 74

Soil oxidoreductase zymography: Visualizing spatial distributions of peroxidase and phenol oxidase activities at the root-soil interface

Decomposition of organic material in the rhizosphere – the most dynamic microbial habitat in soil – involves arrays of oxidoreductase and hydrolytic enzymes. Spatial distributions of various hydrolytic activities in soil have already been explored by zymographic techniques. However, the distribution of oxidative activity in the rhizosphere remains to be studied. Thus, we extended a Time-Lapse Zymography technique, using Amplex Red® reagent, to visualize and quantify distributions of phenol oxidase and peroxidase activities in the rhizosphere of Zea mays L. growing in a Haplic Phaeozem and the non-rhizospheric soil. The gross oxidative activity was greatest at the root surfaces, and fell to background soil levels 1.26 and 0.73 mm from seminal (>1 mm diameter) and lateral (<0.5 mm diameter) roots, respectively. The rhizosphere extent relative to the root radius was 59% broader around lateral than around seminal roots. The greatest activities, up to 30 nmol cm –2 min –1 , were peroxidase-dominated and closely associated with roots. Here, the results confirm the utility of the approach for studying spatio-temporal distributions of oxidative activities in soil. However, actual activity of oxidoreductases in the field will be strongly controlled by fluctuating environmental conditions such as soil aeration and the gradient of reactive oxygen species, which need to be considered especially in anoxic soils.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Inhibition of Extracellular Enzyme Activity by Reactive Oxygen Species upon Oxygenation of Reduced Iron-Bearing Minerals

The dual roles of minerals in inhibiting and prolonging extracellular enzyme activity in soils and sediments are governed by enzyme adsorption to mineral surface. Oxigenation of mineral-bound Fe(II) generates reactive oxygen species (ROS), yet it is unknown whether and how this process alters the activity and functional lifespan of extracellular enzymes. Here, the effect of mineral-bound Fe(II) oxidation on the hydrolytic activity of a cellulose-degrading enzyme ß-glucosidase (BG) was studied using two pre-reduced Fe-bearing clay minerals (nontronite and montmorillonite) and one pre-reduced iron oxide (magnetite) at pH 5 and 7. Under anoxic condition, BG adsorption to mineral surface decreased its activity but prolonged its lifespan. Under oxic condition, ROS was produced, with the amount of •OH, the most abundant ROS, being positively correlated with the extent of structural Fe(II) oxidation in reduced minerals. •OH decreased BG activity and shortened its lifespan via conformational change and structural decomposition of BG. These results suggest that under oxic condition the ROS-induced inhibitory role of Fe(II)-bearing minerals outweighed their adsorption-induced protective role in controlling enzyme activity. These results disclose a previously unknown mechanism of extracellular enzyme inactivation, which have pivotal implications for predicting the active enzyme pool in redox-oscillating environments.

58 GEOSCIENCES↗

Discovery of Aminopiperidine Indoles That Activate the Guanine Nucleotide Exchange Factor SOS1 and Modulate RAS Signaling

Deregulated RAS activity, often the result of mutation, is implicated in approximately 30% of all human cancers. Despite this statistic, no clinically successful treatment for RAS-driven tumors has yet been developed. One approach for modulating RAS activity is to target and affect the activity of proteins that interact with RAS, such as the guanine nucleotide exchange factor (GEF) son of sevenless homologue 1 (SOS1). In this paper, we report on structure–activity relationships (SAR) in an indole series of compounds. Using structure-based design, we systematically explored substitution patterns on the indole nucleus, the pendant amino acid moiety, and the linker unit that connects these two fragments. Best-in-class compounds activate the nucleotide exchange process at submicromolar concentrations in vitro, increase levels of active RAS–GTP in HeLa cells, and elicit signaling changes in the mitogen-activated protein kinase–extracellular regulated kinase (MAPK–ERK) pathway, resulting in a decrease in pERK1/2 T202/Y204 protein levels at higher compound concentrations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Structure–Activity Relationships for Ethanol Dehydrogenation to Acetaldehyde by Silica-Supported Zinc Oxide Catalysts

Silica-supported ZnO efficiently catalyzes the nonoxidative dehydrogenation of ethanol to acetaldehyde, which is relevant for production of 1,3-butadiene from bioethanol. Characterization with in situ spectroscopies under dehydrated conditions (high sensitivity-low energy ion scattering (HS-LEIS), diffuse reflectance (DR) UV–vis, X-ray absorption spectroscopy (XAS), diffuse reflectance Fourier transform infrared spectroscopy (DRIFTS), inelastic neutron scattering (INS), and UV Raman), and ammonia adsorption probed by temperature-programmed desorption followed by DRIFTS and mass spectrometry (DRIFTS-MS NH 3 -TPD), and DFT calculations revealed that the supported ZnO x phase was present as isolated surface ZnO x sites on SiO 2 , with the vast majority coordinated by two siloxane bonds and one silicon atom with two nonbridging oxygens ((≡SiO) 2 Zn 2+ O 2 Si=), anchored at 4-, 5-, and 6-membered siloxane rings. A minor fraction of surface ZnO x sites possessed Lewis acidity, and even fewer sites possessed a Bro̷nsted acidic Zn(OH) + Si moiety. Ethanol temperature-programmed surface reaction-mass spectrometry (TPSR-MS) with various oxidative or ethanol reaction pretreatments indicated that only sites with Lewis and Bro̷nsted acidic character (Zn(OH) + Si) were active for ethanol dehydrogenation, while the majority surface (≡SiO) 2 Zn 2+ O 2 Si= sites were inactive. Greater heterogeneity among all surface ZnO x sites, as assessed by in situ DR UV–vis spectroscopy, was associated with a greater number of ZnO x sites that were active for ethanol dehydrogenation as well as lower enthalpic barriers for acetaldehyde production among the most active surface ZnO x sites. Turnover frequencies and the apparent activation energy for ethanol dehydrogenation were determined from steady-state kinetics. Together, these findings suggested that anchoring inactive surface (≡SiO) 2 Zn 2+ O 2 Si= sites on the silica support caused a greater number of active surface ZnO x sites to adopt a more strained configuration, promoting ethanol dehydrogenation catalysis. Pretreatments and catalysts that promoted desorption of ethanol during TPSR, taken as a marker of surface dehydroxylation, were associated with an increased number of the most active surface (Zn(OH) + Si) sites. Such findings suggested that inactive surface ZnO x sites were activated for ethanol dehydrogenation by dehydroxylation of the support and/or decreased coordination to hemilabile siloxane ligands.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Propene Metathesis over Supported Tungsten Oxide Catalysts: A Study of Active Site Formation

A detailed investigation was conducted on the factors influencing the properties of silica-supported tungsten oxide catalysts for propene metathesis. A principal goal of this work was to identify the processes involved in the formation of catalytically active sites. To probe the influence of dispersion, samples were prepared across a range of W loadings using two methods of catalyst preparation: incipient wetness impregnation of amorphous silica and ion exchange of mesoporous SBA-15. The samples were characterized by nitrogen adsorption, UV-vis, Raman, and X-ray absorption spectroscopy (XAS). Catalytic activity was observed to increase with W surface concentration up to the point where WO 3 nanoparticles formed. The catalytic performance of all samples was enhanced 2-fold by pretreatment in He, in comparison to pretreatment in air. In situ characterization of samples pretreated in He by Raman and XAS shows an increase in the relative concentration of isolated dioxo W(6+) species relative to mono-oxo W(6+) species, and in situ XAS data collected during propene metathesis indicated that a similar conversion occurs for air-pretreated samples in the presence of propene. For both air- and He-pretreated catalysts an activation period was observed, during which the activity increased and attained steady-state activity. This period was significantly longer for air-pretreated catalysts and was accompanied by the transient formation of acetone. While acetone was not observed during the much shorter transient of He-pretreated samples, in situ XAS provided evidence of reduction occurring in these samples upon contact with propene. In conclusion, it is also notable that, independent of the manner of catalyst preparation or pretreatment, the rate of propene metathesis is first order in propene and exhibits an activation energy of 200 kJ/mol. A model is proposed to explain why only a fraction of the isolated tungstate species is active for propene metathesis (~5%) and why this fraction increases with increasing concentration of W dispersed on silica.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Cooperative H 2 Activation across a Metal–Metal Multiple Bond and Hydrogenation Reactions Catalyzed by a Zr/Co Heterobimetallic Complex

In the quest for active and selective catalysts featuring nonprecious metals, bimetallic cooperativity poses a unique opportunity to promote catalytic reactions and influence selectivity. While examples of stoichiometric H 2 activation across metal–metal bonds have been reported, there have been limited advances toward the incorporation of a well-defined cooperative bimetallic H 2 activation process into a catalytic cycle for the hydrogenation of unsaturated hydrocarbons. We demonstrate that facile activation of H 2 by two nonprecious metals is facilitated by metal–metal cooperativity in the coordinatively unsaturated Zr/Co bis(phosphinoamide) complexes (THF)(I)Zr(XylNP i Pr 2 ) 2 CoPR 3 (3-PMe 3 and 3-PMePh 2 , Xyl =3,5-dimethylphenyl), which feature highly polar Zr–Co triple bonds. Owing to the stabilizing nature of the metal–metal bond, the H 2 activation products (THF)(I)Zr(μ-H)(XylNP i Pr 2 ) 2 Co(H)(PR 3 ) (4-PMe 3 and 4-PMePh 2 ), which feature one terminally bound Co hydride and one hydride bridging the two metals, have been isolated and crystallographically characterized. The Zr/Co bimetallic complex 3-PMePh 2 is an active catalyst for the hydrogenation of alkenes and semihydrogenation of alkynes, and relevant intermediates including 4-PMePh 2 and alkene (5) and alkyne (6) adducts have been identified spectroscopically in situ and isolated and characterized through independent synthesis. The alkyne semihydrogenation reaction catalyzed by 3-PMePh 2 exhibits high selectivity for alkene over alkane products and generates an unselective distribution of (E)- and (Z)-alkenes via direct formation of both stereoisomers. These findings lend insight into the roles that metal–metal bonding and cooperativity play in the activation of small molecules and the promotion and selectivity of subsequent catalytic transformations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Interdependence in active mobility adoption: Joint modeling and motivational spillover in walking, cycling and bike-sharing

Active mobility offers an array of physical, emotional, and social well-being benefits. However, with the proliferation of the sharing economy, new nonmotorized means of transport are entering the fold, complementing some existing mobility options while competing with others. The purpose of this research study is to investigate the adoption of three active travel modes—namely walking, cycling, and bikesharing—in a joint modeling framework. Here, the analysis is based on an adaptation of the stages of change framework, which originates from the health behavior sciences. Multivariate ordered probit modeling drawing on U.S. survey data provides well-needed insights into individuals’ preparedness to adopt multiple active modes as a function of personal, neighborhood, and psychosocial factors. The research suggests three important findings. (1) The joint model structure confirms interdependence among different active mobility choices. The strongest complementarity is found for walking and cycling adoption. (2) Each mode has a distinctive adoption path with either three or four separate stages. We discuss the implications of derived stage-thresholds and plot adoption contours for selected scenarios. (3) Psychological and neighborhood variables generate more coupling among active modes than individual and household factors. Specifically, identifying strongly with active mobility aspirations, experiences with multimodal travel, possessing better navigational skills, along with supportive local community norms are the factors that appear to drive the joint adoption decisions. This study contributes to the understanding of how decisions within the same functional domain are related and help to design policies that promote active mobility by identifying positive spillovers and joint determinants.

42 ENGINEERING↗

Active site plasticity and possible modes of chemical inhibition of the human DNA deaminase APOBEC3B

The single-stranded DNA cytosine deaminase APOBEC3B (A3B) functions in innate immunity against viruses, but it is also strongly implicated in eliciting mutations in cancer genomes. Because of the critical role of A3B in promoting virus and tumor evolution, small molecule inhibitors are desirable. However, there is no reported structure for any of the APOBEC3-family enzymes in complex with a small molecule bound in the active site, which hampers the development of small molecules targeting A3B. Here we report high-resolution structures of an active A3B catalytic domain chimera with loop 7 residues exchanged with those from the corresponding region of APOBEC3G (A3G). The structures reveal novel open conformations lacking the catalytically essential zinc ion, with the highly conserved active site residues extensively rearranged. These inactive conformations are stabilized by 2-pyrimidone or an iodide ion bound in the active site. Molecular dynamics simulations corroborate the remarkable plasticity of the engineered active site and identify key interactions that stabilize the native A3B active site. These data provide insights into A3B active site dynamics and suggest possible modes of its inhibition by small molecules, which would aid in rational design of selective A3B inhibitors for constraining virus and tumor evolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Temperature modulation of CAMTA3 gene induction activity is mediated through the DNA binding domain

The calmodulin-binding transcription activator (CAMTA) proteins of Arabidopsis thaliana play a major role in cold acclimation, contributing to the rapid induction of the C-REPEAT BINDING FACTOR (CBF) genes and other genes that impart freezing tolerance in plants exposed to cold temperature (4°C). The goal of this study was to better understand how the gene induction activity of CAMTA3 is modulated by temperature. Our results indicate that a severely truncated version of CAMTA3, CAMTA3 334 , which includes the N-terminal CG-1 DNA binding domain and a newly identified transcriptional activation domain (TAD), was able to rapidly induce the expression of CBF2 and two newly identified target genes, EXPANSIN-LIKE A1 (EXPL1) and NINE-CIS-EPOXYCAROTENOID DIOXYGENASE 3 (NCED3), in response to cold temperature. Additionally, CAMTA3 334 was able to restore freezing tolerance when expressed in a camta23 double mutant. The ability of CAMTA3 and CAMTA3 334 to induce target genes at cold temperature did not involve increased levels of these proteins or increased binding of these proteins to target gene promoters in cold-treated plants. Rather, domain-swapping experiments indicated that the CAMTA3 CG-1 domain conferred temperature dependence to the ability of the CAMTA3 TAD to induce gene expression. The CG-1 domain also enabled the TAD to induce the expression of target genes at a moderate temperature (22°C) in response to cycloheximide treatment, consistent with the TAD activity not being intrinsically temperature dependent. We propose a working model in which the temperature modulation of CAMTA3 gene induction activity occurs independently from the C-terminal calmodulin-binding domains that previously have been proposed to activate CAMTA3 transcriptional activity in response to cold temperature.

59 BASIC BIOLOGICAL SCIENCES↗

Arrested-motility states in populations of shape-anisotropic active Janus particles

The emergence of large-scale collective phenomena from simple interactions between individual units is a hallmark of active matter systems. Active colloids with alignment-dominated interparticle interactions tend to develop orientational order and form motile coherent states, such as flocks and swarms. Alternatively, a combination of self-propulsion and excluded-volume interactions results in self-trapping and active phase separation into dense clusters. Here, we reveal unconventional arrested-motility states in ensembles of active discoidal particles powered by induced-charge electrophoresis. Combining experiments and computational modeling, we demonstrate that the shape asymmetry of the particles promotes the hydrodynamically assisted formation of active particles’ bound states in a certain range of excitation parameters, ultimately leading to a spontaneous collective state with arrested motility. Unlike the jammed clusters obtained through self-trapping, the arrested-motility phase remains sparse, dynamic, and reconfigurable. The demonstrated mechanism of phase separation seeded by bound state formation in ensembles of oblate active particles is generic and should be applicable to other active colloidal systems.

42 ENGINEERING↗

Avian Activity Classification Using Recurrent Networks to Fuse Videos with Metadata on Imbalanced Datasets

Activity classification plays a crucial role in various real-life scenarios involving both humans and animals. There is an increasing need for precise activity classification focused on avian-solar interactions, as the usage of solar energy facilities, such as photovoltaic array power stations, has been observed to impact bird species richness, behavior, and activity. However, there has been no work to develop an automated system to monitor and classify these avian-solar interactions. All current methods rely on human observers, which is time and human resources costly and subject to errors related to searcher efficiency. With the recent success of Deep Learning models in activity classification problems, this paper develops a recurrent neural network-based model to automatically classify six avian activities around solar energy facilities. Our proposed model integrates critical feature engineering metadata with video frame data, enabling improved learning and more accurate activity classification. Furthermore, we address the challenge of data imbalance during training and demonstrate the efficacy of our model in detecting and classifying different activities within video tracks. Additionally, we analyze the saliency/backpropagation map of the trained proposed model and validate its decision-making rationale.

Avian activity classification; bidirectional LSTM;↗

Study of Gamma-ray Production from Neutron-induced Activity on Spacecraft for DIORAMA

The Distributed Infrastructure Offering Real{Time Access to Modeling and Analysis (DIORAMA) software provides performance modeling capabilities of the United States Nuclear Detonation Detection System (USNDS) with a focus on the characterization of Space{Based Nuclear Detonation Detection (SNDD) instrument performance [1]. A study was conducted investigating the neutron activation on spacecraft caused by cosmic{ray and trapped proton{induced spallation, and by direct irradiation from hypothetical nuclear detonations as a background component to be incorporated into DIORAMA. The neutron activation background study amasses a database of pre-calculated Monte Carlo data that reflects the delayed gamma-ray production from activated nuclei as a function of incident cosmic or neutron flux, incident energy, material volume, etc. CINDER2008 coupled with MCNPX and the Activation Analysis Tools in the Activation in Accelerator Radiation Environments (AARE) distribution along with a suite of Python scripts are used to calculate and assemble the Monte Carlo database [2{6]. The neutron activation background study improves the fidelity of DIORAMA by providing the neutron-induced activation component to the background of the instruments on-board the spacecraft, and has implications for future instrument design studies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Active Subspace Methods for Data-Intensive Inverse Problems (Final Report)

Fostered by advances in computing, simulation and data science have emerged as the third and fourth paradigms enabling scientific discovery. Many mathematical challenges arise when integrating these paradigms to produce new knowledge. One of the most important challenges is the inverse or calibration problem: find the inputs of a simulation such that its outputs agree with a given set of measurements. As both the cost of the complex simulations and the volume of measurement data sets increase, intuition-driven trial-and-error methods for calibration quickly become untenable; modern scientists need more computationally efficient and mathematically rigorous methods. When measurements and simulated predictions contain uncertainty, the Bayesian perspective on statistical inference provides a popular mathematical framework for developing computational methods to solve inverse problems. Markov chain Monte Carlo (MCMC) methods are a mainstay of this framework; they characterize a probability distribution on the space of model inputs that is conditioned on the given measurements. However, despite recent advances, MCMC methods remain impractical for expensive simulations with more than a handful of inputs. At its heart, MCMC searches the space of inputs for corresponding outputs that match measurements, and so it is afflicted with the curse of dimensionality. Additionally, MCMC methods become less efficient as the volume of the measurement data increases, particularly if the measurements are redundant. To make MCMC—and Bayesian methods, in general—amenable to today’s computer models with millions of inputs and terabytes of measurement data, one must devise methods to reduce the dimension of both the simulation inputs and the measurements. For many models used in practice, the outputs vary primarily on a low-dimensional manifold of the nominally high-dimensional input space. Recently developed active subspace methods discover this low-dimensional manifold and exploit it to enable analyses typically suited for low-dimensional settings (e.g., statistical inversion of complex systems). We hypothesize that active subspace methods can provide the necessary dimension reduction, enabling existing Bayesian methods to find solutions for a large class of otherwise intractable statistical inverse problems. We will test the hypothesis with a series of mathematical analyses and computational experiments that will proceed along three research veins: (i) integrating active subspace methods into methods for statistical inversion, (ii) addressing current challenges in active subspace methods, and (iii) incorporating methods for nonlinear dimensionality reduction into active subspace methods. We will apply the methods we develop to real inverse problems in chemical kinetics and turbulent flame modeling. Our team combines expertise in methods for inverse problems, dimension reduction, and reduced order modeling. With a history of active collaboration and thoughtful engagement, we bring enthusiasm and fresh perspectives to a well studied subject. We are excited by the potential opportunity to work together to extend and apply active subspace methods to data-intensive inverse problems.

97 MATHEMATICS AND COMPUTING↗

Active Subspace Methods for Data-Intensive Inverse Problems

Fostered by advances in computing, simulation and data science have emerged as the third and fourth paradigms enabling scientific discovery. Many mathematical challenges arise when integrating these paradigms to produce new knowledge. One of the most important challenges is the inverse or calibration problem: find the inputs of a simulation such that its outputs agree with a given set of measurements. As both the cost of the complex simulations and the volume of measurement data sets increase, intuition-driven trial-and-error methods for calibration quickly become untenable; modern scientists need more computationally efficient and mathematically rigorous methods. When measurements and simulated predictions contain uncertainty, the Bayesian perspective on statistical inference provides a popular mathematical framework for developing computational methods to solve inverse problems. Markov chain Monte Carlo (MCMC) methods are a mainstay of this framework; they characterize a probability distribution on the space of model inputs that is conditioned on the given measurements. However, despite recent advances, MCMC methods remain impractical for expensive simulations with more than a handful of inputs. At its heart, MCMC searches the space of inputs for corresponding outputs that match measurements, and so it is afflicted with the curse of dimensionality. Additionally, MCMC methods become less efficient as the volume of the measurement data increases, particularly if the measurements are redundant. To make MCMC—and Bayesian methods, in general—amenable to today’s computer models with millions of inputs and terabytes of measurement data, one must devise methods to reduce the dimension of both the simulation inputs and the measurements. For many models used in practice, the outputs vary primarily on a low-dimensional manifold of the nominally high-dimensional input space. Recently developed active subspace methods discover this low-dimensional manifold and exploit it to enable analyses typically suited for low-dimensional settings (e.g., statistical inversion of complex systems). We hypothesize that active subspace methods can provide the necessary dimension reduction, enabling existing Bayesian methods to find solutions for a large class of otherwise intractable statistical inverse problems. We will test the hypothesis with a series of mathematical analyses and computational experiments that will proceed along three research veins: (i) integrating active subspace methods into methods for statistical inversion, (ii) addressing current challenges in active subspace methods, and (iii) incorporating methods for nonlinear dimensionality reduction into active subspace methods. We will apply the methods we develop to real inverse problems in chemical kinetics and turbulent flame modeling. Our team combines expertise in methods for inverse problems, dimension reduction, and reduced order modeling. With a history of active collaboration and thoughtful engagement, we bring enthusiasm and fresh perspectives to a well studied subject. We are excited by the potential opportunity to work together to extend and apply active subspace methods to data-intensive inverse problems.

97 MATHEMATICS AND COMPUTING↗

Performance and Economic Analysis of Organosolv Softwood and Herbaceous Lignins to Activated Carbons as Electrode Materials in Supercapacitors

In this work, yellow pine (YP, softwood) and switchgrass (SG, grass) lignins were extracted as a coproduct of an organosolv γ-valerolactone (GVL) biorefinery that also produces biofuels and furfural. The extracted lignins were converted to carbon precursors for synthesizing porous activated carbon electrodes for high energy-density supercapacitors. This research details the impact of lignin composition on the derived porous structures and electrochemical properties of activated carbons. Lignin precursors with various syringyl (S) to guaiacyl (G) contents were characterized using 31 P nuclear magnetic resonance (NMR) and two-dimensional 1 H‒ 13 C NMR. A two-step activation process, using steam and carbon dioxide as the activating agents, enabled the formation of porous carbons structures with high surface area. The capacitive behavior of supercapacitors was systematically characterized by cyclic voltammetry, charge-discharge cycling, and electrochemical impedance spectroscopy. The specific capacitance of YP and SG capacitors reached 367 and 221 F g −1 , respectively. Both types of capacitors demonstrated remarkably stable capacitance (capacitance retention >90%) along with excellent Coulombic efficiency (>99%) over 10,000 cycles. Compared to SG electrode, the better electrochemical performance achieved with YP electrodes was mainly due to shorter diffusion path, improved ionic mobility, and increased active surface area. The inexpensive lignin-based porous electrodes synthesized in this work can be used for various electrochemical devices for improved performance, decreased cost, and enhanced durability. This work also demonstrates that the selection of feedstock and appropriate processing conditions can tailor the structure of carbon composites for targeted applications. Techno-economic analysis indicates that YP and SG activated carbons can be produced at a minimum selling price of $8,493 and $6,670 per ton, respectively, which is competitive with the commercially available supercapacitor-grade activated carbons.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Consensus Modeling for Prediction of Estrogenic Activity of Ingredients Commonly Used in Sunscreen Products

Sunscreen products are predominantly regulated as over-the-counter (OTC) drugs by the US FDA. The “active” ingredients function as ultraviolet filters. Once a sunscreen product is generally recognized as safe and effective (GRASE) via an OTC drug review process, new formulations using these ingredients do not require FDA review and approval, however, the majority of ingredients have never been tested to uncover any potential endocrine activity and their ability to interact with the estrogen receptor (ER) is unknown, despite the fact that this is a very extensively studied target related to endocrine activity. Consequently, we have developed an in silico model to prioritize single ingredient estrogen receptor activity for use when actual animal data are inadequate, equivocal, or absent. It relies on consensus modeling to qualitatively and quantitatively predict ER binding activity. As proof of concept, the model was applied to ingredients commonly used in sunscreen products worldwide and a few reference chemicals. Of the 32 chemicals with unknown ER binding activity that were evaluated, seven were predicted to be active estrogenic compounds. Five of the seven were confirmed by the published data. Further experimental data is needed to confirm the other two predictions.

60 APPLIED LIFE SCIENCES↗

Activity in the ferret: oestradiol effects and circadian rhythms

The present study was conducted to determine whether oestradiol increases activity in the European ferret (Mustela furo), whether this effect is sexually dimorphic, and whether a 24-h rhythm is present in the ferret's daily activity. The activity of male and female adult, postpubertally gonadectomized ferrets was monitored while they were maintained singly on a 13:11 light-dark cycle, before and after implantation with oestradiol-17 beta. Gonadectomized male and female ferrets exhibited equal levels of activity, and neither sex exhibited a significant change in activity following oestradiol implantation. None of the ferrets exhibited a strong circadian rhythm, although weak 24-h rhythms and shorter harmonic rhythms were present. Golden hamsters (Mesocricetus auratus), monitored in an identical manner, exhibited strong circadian rhythms. It was concluded that oestradiol administration may not cause an increase in activity in the ferret, and that this species lacks a strong circadian activity rhythm.

Non-NASA Center↗

Determining the amount of physical activity needed for long-term weight control

OBJECTIVE: To evaluate prospectively the influence of habitual physical activity on body weight of men and women and to develop a model that defines the role of physical activity on longitudinal weight change. DESIGN AND SETTING: Occupational cohort study conducted for a mean of 5.5 y. SUBJECTS: A total of 496 (341 male and 155 female) NASA/Johnson Space Center employees who completed the 3 month education component of the employee health-related fitness program and remained involved for a minimum of 2 y. MEASUREMENTS: Body weights were measured at baseline (T1) and follow-up (T2), and habitual physical activity was obtained from the mean of multiple ratings of the 11-point (0-10) NASA Activity Scale (NAS) recorded quarterly between T1 and T2. Other measures included age, gender, VO(2 max) obtained from maximal treadmill testing, body mass index (BMI), and body fat percentage. RESULTS: Multiple regression demonstrated that mean NAS, T1 weight, aging and gender all influence long-term T2 weight. T1 age was significant for the men only. Independently, each increase in mean NAS significantly (P<0.01) reduced T2 weight in men (b=-0.91 kg; 95% CI:-1.4 to-0.42 kg) and women (b=-2.14 kg; 95% CI:-2.93 to-1.35 kg). Mean NAS had a greater effect on T2 weight as T1 weight increased, and the relationship was dose-dependent. CONCLUSIONS: Habitual physical activity is a significant source of long-term weight change. The use of self-reported activity level is helpful in predicting long-term weight changes and may be used by health care professionals when counseling patients about the value of physical activity for weight control.

Exercise↗