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This work introduces a knowledge-informed framework that integrates human expertise and prior interest into active learning-driven autonomous experimentation, which enhances the exploration efficiency through more targeted experimentation.
Spectrofluorimetric studies on active site of alpha-chymotrypsin using anthraniloyl as chromophere
Spectrofluorimetric analysis of mobility and polarity of active sites of alpha chymotrypsin
A neutral gas mass spectrometer was flown to Venus as part of the Pioneer Venus Multiprobe to measure the composition of its lower atmosphere. The instrument, mounted in the Sounder Probe, was activated after the probe entered the top of the atmosphere, and it obtained data during the descent from 62 km to the surface. Atmospheric gases were sampled through a pair of microleaks, the effluent from which was pumped by a combination of ion and getter pumping. A pneumatically operated valve, controlled by the ambient atmospheric pressure, maintained the ion source pressure at a nearly constant value during descent while the atmospheric pressure varied by three orders of magnitude. A single focusing magnetic sector field mass spectrometer with mass resolution sufficient to reasonably separate argon from C3H4 at 40 amu provided the mass analysis and relative abundance measurements. A microprocessor controlled the operation of the mass spectrometer through a highly efficient peak-tip stepping routine and data compression algorithm that effected a scan of the mass spectrum from 1 to 208 amu in 64 sec while requiring an information rate of only 40 bits/sec to return the data to earth. A subscale height altitude resolution was thus obtained. Weight, size, and power requirements were minimized to be consistent with interplanetary flight contraints.
Multi-omic analyses can provide a great deal of information on the potential for activity within a microbial community but often lack specificity to link functions to cell and primarily offer potential for function, not true expression of function. Functional assays are necessary for understanding in situ microbial activity to better describe and improve microbiome biology. Targeting enzyme activity through activity-based protein profiling enhances the accuracy of functional studies. Here, we introduce PhenoSelection, a pipeline of coupling activity-based probing with fluorescence-activated cell sorting and downstream activity assays to isolate and examine viable populations of cells expressing a function of interest. We applied PhenoSelection to a soil microbiome to enrich for communities with elevated activity for lignocellulose-degradation phenotypes. PhenoSelection was able to separate and identify taxonomic members with activity for glycosyl hydrolases, and expanding to various probes for other function, this process can be applied to unique phenotypes of interest.
Microbial catabolism of chitin, a highly abundant component of polymeric organic matter worldwide, is a fundamental cog in the carbon and nitrogen cycles that govern CO2 emissions, nutrient dynamics in aquatic systems, and soil vitality. Despite this critical biogeochemical function, we have a poor understanding of the synergy between various microbial chitinases and key accessory enzymes involved in chitin catabolism. We designed and synthesized activity-based probes (ABPs) for profiling active chitinolytic enzymes by modifying the chitin derivatives N-acetyl glucosamine and chitotriose. The ABPs were used to determine the active complement of chitinolytic enzymes produced over time by the soil bacterium Cellvibrio japonicus treated with various C substrates. This approach allowed us to demonstrate the utility of these ABPs in determining the intricate synergy between chitinases and other glycoside hydrolases that microbes employ to catabolize chitin. The strategy can be used to gain molecular level insights that can be used to better understand microbial roles in soil C and N cycling in the face of a changing climate.
Telomerase has long been considered as a biomarker for cancer diagnosis and a therapeutic target for drug discovery. Detecting telomerase activity in vivo could provide more direct information of tumor progression and response to drug treatment, which, however, is hampered by the lack of an effective probe that can generate an output signal without a tissue penetration depth limit. Here in this study, using the principle of distance-dependent magnetic resonance tuning, we constructed a telomerase-activated magnetic resonance imaging probe (TAMP) by connecting superparamagnetic ferroferric oxide nanoparticles (SPFONs) and paramagnetic Gd-DOTA (Gd(III) 1,4,7,10- tetraazacyclododecane-1,4,7,10-tetraazacyclododecane-1,4,7,10-tet-raacetic acid) complexes via telomerase-responsive DNA motifs. Upon telomerase-catalyzed extension of the primer in TAMP, Gd-DOTA-conjugated oligonucleotides can be liberated from the surface of SPFONs through a DNA strand displacement reaction, restoring the T 1 signal of the Gd-DOTA for a direct readout of the telomerase activity. Here we show that, by tracking telomerase activity, this probe provides consistent monitoring of tumor growth kinetics during progression and in response to drug treatment and enables in situ screening of telomerase inhibitors in whole-animal models. This study provides an alternative toolkit for cancer diagnosis, treatment response assessment, and anticancer drug screening.
As a result of a number of large multiwavelength monitoring campaigns that have taken place since the late 1980s, there are now several very large data sets on bright variable active galactic nuclei (AGNs) that are well-sampled in time and can be used to probe the physics of the AGN continuum source and the broad-line emitting region. Most of these data sets have been underutilized, as the emphasis thus far has been primarily on reverberation-mapping issues alone. Broader attempts at analysis have been made on some of the earlier IUE data sets (e.g., data from the 1989 campaign on NGC5 548) , but much of this analysis needs to be revisited now that improved versions of the data are now available from final archive processing. We propose to use the multiwavelength monitoring data that have been accumulated to undertake more thorough investigations of the AGN continuum and broad emission lines, including a more detailed study of line-profile variability, making use of constraints imposed by the reverberation results.
This report discusses a low cost, easy to develop instrument to measure magnetic fields in pulsed solenoids used for the MagNIF pulse power system. The instrument is designed to integrate voltage signals from custom B-dot probes made from PCB inductor chips. The instrument is battery powered with ± 9V rails. Acceptable B-dot probe inputs range from mV to 20V with frequencies up to 4MHz. Details of the circuit design and practical considerations for working with high speed signals are discussed.
Volume 3 assists Earth Observing System (EOS) investigators in locating required non-EOS data products by identifying their non-EOS input requirements and providing the information on data sets available at various Distributed Active Archive Centers (DAAC's), including those from Pathfinder Activities and Earth Probes. Volume 3 is intended to complement, not to duplicate, the the EOSDIS Science Data Plan (SDP) by providing detailed data set information which was not presented in the SDP. Section 9 of this volume discusses the algorithm summary tables containing information on retrieval algorithms, expected outputs and required input data. Section 10 describes the non-EOS input requirements of instrument teams and IDS investigators. Also described are the current and future data holdings of the original seven DAACS and data products planned from the future missions and projects including Earth Probes and Pathfinder Activities. Section 11 describes source of information used in compiling data set information presented in this volume. A list of data set attributes used to describe various data sets is presented in section 12 along with their descriptions. Finally, Section 13 presents the SPSO's future plan to improve this report .
Electromagnetic ion cyclotron (EMIC) waves play a key role in radiation belt dynamics through resonant interactions. However, their low occurrence probability, high variability, and spatial intermittency pose challenges for accurate modeling. In this study, we present a machine learning (ML)-based global EMIC wave model built on the entire data set from the Van Allen Probes mission. To capture the distinct statistical characteristics of wave occurrence and amplitude, the model is separated into two modules: an occurrence model trained using ML techniques, and a wave amplitude model sampled from observed probability distributions. The input parameters are limited to real-time or predictable variables to ensure practical applicability. Our model shows strong performance across the entire test set and demonstrates improved predictive capability over a baseline random occurrence model, particularly during quiet geomagnetic conditions. Evaluation during both quiet and active periods confirms the model's ability to represent the clustered and intermittent nature of EMIC wave activity. Furthermore, the model provides global estimates of wave power, enabling integration with radiation belt electron data and showing signatures consistent with wave-induced scattering. We found a good correlation between the global wave activity from the model and relativistic electron observation by Van Allen Probes, regardless of the availability of in situ wave observations. The modular structure of the model also allows for straightforward expansion for additional wave properties, such as wave frequency, which can be modeled independently. This flexible, event-sensitive approach offers a promising framework for data-driven radiation belt simulations and space weather applications.
Recent progress in machine learning methods and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs) have propelled automated and autonomous microscopies to the forefront of attention of the scientific community. However, enabling automated microscopy requires the development of task-specific machine learning methods, understanding the interplay between physics discovery and machine learning, and fully defined discovery workflows. This, in turn, requires balancing the physical intuition and prior knowledge of the domain scientist with rewards that define experimental goals and machine learning algorithms that can translate these to specific experimental protocols. Here, we discuss the basic principles of Bayesian active learning and illustrate its applications for SPM. We progress from the Gaussian process as a simple data-driven method and Bayesian inference for physical models as an extension of physics-based functional fits to more complex deep kernel learning methods, structured Gaussian processes, and hypothesis learning. These frameworks allow for the use of prior data, the discovery of specific functionalities as encoded in spectral data, and exploration of physical laws manifesting during the experiment. Here, the discussed framework can be universally applied to all techniques combining imaging and spectroscopy, SPM methods, nanoindentation, electron microscopy and spectroscopy, and chemical imaging methods and can be particularly impactful for destructive or irreversible measurements.
A unique plug-type heat flux gage probe was tested in the NASA Ames Research Center 2x9 turbulent flow duct facility. The probe was fabricated by welding a miniature dual active surface heat flux gage body to the end of a hollow metal cylindrical bolt containing a metal inner tube. Cooling air flows through the inner tube, impinges onto the back of the gage body and then flows out through the annulus formed between the inner tube and the hollow bolt wall. Heat flux was generated in the duct facility with a Huels arc heater. The duct had a rectangular cross section and one wall was fabricated from 2.54 centimeter thick thermal insulation rigid surface material mounted onto an aluminum plate. To measure heat flux, the probe was inserted through the plate and insulating materials with the from of the gage located flush with the hot gas-side insulation surface. Absorbed heat fluxes measured with the probe were compared with absorbed heat fluxes measured with six water-cooled reference calorimeters. These calorimeters were located in a water-cooled metal duct wall which was located across from the probe position. Correspondence of transient and steady heat fluxes measured with the reference calorimeters and heat flux gage probe was generally within a satisfactory plus or minus 10 percent. This good correspondence was achieved even though the much cooler probe caused a large surface temperature disruption of 1000K between the metal gage and the insulation. However, this temperature disruption did not seriously effect the accuracy of the heat flux measurement. A current application for dual active surface heat flux gages is for transient and steady absorbed heat flux, surface temperature and heat transfer coefficient measurements on the surface of an oxidizer turbine inlet deflector operating in a space shuttle test bed engine.
In this work, imaging mechanisms in contact Kelvin probe force microscopy (cKPFM) are explored via information theory-based methods. Gaussian processes are used to achieve super-resolution in the cKPFM signal, effectively extrapolating across the spatial and parameter space. Tensor factorization is applied to reduce the multidimensional signal to the tensor convolution of the scalar functions that show a clear trending behavior with the imaging parameters. These methods establish a workflow for the analysis of the multidimensional datasets that can then be related to the relevant physical mechanisms. We also provide an interactive Google Colab notebook that goes through all the analyses discussed in the paper.
The STIS coronagraphic imaging sample of I'MS stars was surveyed with the Goddard Fabry-Perot (GFP) interferometer to determine what fraction of the stars drive jets, whether there is any difference in behavior for a group of intermediate-mass stars as compared with T Tauri stars, and to search for evolutionary effects. Compared to broad band imaging, the FGP achieves an emission-line nebulosity-to-star contrast gain of between 500 and 3000. To date, we have detected jets associated with classical T Tauri stars spanning a factor of 280 in mass accretion rate in approximately 50% of the STIS coronagraphic imaging sample. We also detected jets or Herbig-HARO knots associated with 5 Herbig Ae stars, all younger than 8 Myr, for a detection fraction which is smaller than the T Tauri survey.
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A powerful, new probe of chromospheric activity, cross-correlation, has been developed and applied to a variety of stars. In this particular application, an entire CCD spectrum of an active star is correlated with the spectrum of a narrow-line, inactive star of similar spectral type and luminosity class. Using a number of strong lines in this manner enables the detection of absorption profile asymmetries at moderate resolution (lambda/Delta lambda about 40,000) and S/N 150:1. This technique has been applied to 14 systems mostly RS CVn's, with 10 not greater than nu sin i not greater than 50 km/s and P not less than 7 d. Distortions were detected for the first time in five systems: Sigma Gem, IM Peg, GX Lib, UV Crb, and Zeta And. Detailed modeling, incorporating both spectral line profiles and broad-band photometry, is applied to Sigma Gem. Profile asymmetries for this star are fitted by two high-latitude spots covering 5 percent of the stellar surface. The derived spot temperature of 3400 K is lower than found in previous studies. In addition, two well-known systems have been studied: HD 199178 and V711 Tau. Polar spots are found on both.