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At least 469 records · Page 26

Gallium-doped zinc oxide: nonlinear reflection and transmission measurements and modeling in the ENZ region

Abstract Strong nonlinear materials have been sought after for decades for applications in telecommunications, sensing, and quantum optics. Gallium-doped zinc oxide is a II–VI transparent conducting oxide that shows promising nonlinearities similar to indium tin oxide and aluminum-doped zinc oxide for the telecommunications band. Here we explore its nonlinearities in the epsilon near zero (ENZ) region and show n 2,eff values on the order of 4.5 × 10 −3 cm 2 GW −1 for IR pumping on 200–300 nm thin films. Measuring nonlinear changes in transmission and reflection with a white light source probe in the near-IR while exciting in the near-IR provides data in both time and wavelength. Three films varying in thickness, optical loss, and ENZ crossover wavelength are numerically modeled and compared to experimental data showing agreement for both dispersion and temporal relaxation. In addition, we discuss optimal excitation and probing wavelengths occur around ENZ for thick films but are red-shifted for thin films where our model provides an additional degree of freedom to explore. Obtaining accurate nonlinear measurements is a difficult and time-consuming task where our method in this paper provides experimental and modeled data to the community for an ENZ material of interest.

Ball, Adam (ORCID:0000000318167813)↗

Towards Precision Photometric Type ia Supernova Cosmology With Machine Learning

The revolutionary discovery of dark energy and accelerating cosmic expansion was made with just 42 type Ia supernovae (SNe Ia) in 1999. Since then, large synoptic surveys, e.g., Dark Energy Survey (DES), have observed thousands more SNe Ia and the upcoming Rubin Legacy Survey of Space and Time (LSST) and Roman Space Telescope promise to deliver millions in the next decade. This unprecedented data volume can produce the required precision to unambiguously test concordance cosmology which could represent a monumental shift in our understanding of dark energy and its role in cosmic history. However, extracting a pure SN Ia sample with accurate redshifts for such a large dataset will be a challenge. Specifically, spectroscopic classification will not be possible for the vast majority of discovered objects, and only ~25% will have spectroscopic redshifts. This thesis presents a series of observational and methodological studies designed to address the questions associated with this new era of photometric SN Ia cosmology. First, we present a machine learning method for photometric classification of SNe, Supernova Classification with a COnvolutional Neural Network (SCONE). Photometric classification enables SNe with no spectroscopic information to be confidently categorized, and is a critical component of current and future analysis pipelines. SCONE achieves >99% accuracy distinguishing simulated SNe Ia from non-Ia SNe, and has been integrated into DES, LSST, and Roman analysis pipelines. We also demonstrate the efficacy of SCONE on early-time photometric classification, which will be vital for optimal allocation of spectroscopic resources. We show that SCONE can distinguish between 6 SN types with 75% accuracy on the night of initial discovery, comparable to results in the literature for full-phase SNe. Next, we study current methods for estimating SN Ia redshifts and propose a machine learning alternative that uses SN photometry alone to extract redshift information. Most SNe Ia inherit redshift information from their host galaxy, but the process of matching SNe to the correct host galaxy can be challenging. We systematically analyze the impact of incorrect redshifts from host galaxy mismatch on 5 years of DES SN data, and conclude that improved host matching or redshift estimation methods can reduce our systematic errors by ~10%. In response to this finding, we present a SN photometry-only method for estimating redshifts independent of host galaxy information, Photo-zSNthesis. We show that Photo-zSNthesis redshift estimates are accurate to within 2% across the full redshift range of LSST, a first in the literature. Finally, we focus on the robustness of machine learning (ML) algorithms for real-world and scientific applications. ML models generalize poorly beyond their training set and often experience severe performance degradation when deployed on new data. We demonstrate a general method for improving robustness that achieves new state-of-the-art results on astronomical object classification, wildlife identification, and tumor detection.

79 ASTRONOMY AND ASTROPHYSICS↗

High‐Performance Fluorinated Fused‐Ring Electron Acceptor with 3D Stacking and Exciton/Charge Transport

Abstract A new fluorinated electron acceptor (FINIC) based on 6,6,12,12‐tetrakis(3‐fluoro‐4‐hexylphenyl)‐indacenobis(dithieno[3,2‐ b ;2 ′ ,3 ′ ‐ d ]thiophene) as the electron‐donating central core and 5,6‐difluoro‐3‐(1,1‐dicyanomethylene)‐1‐indanone as the electron‐deficient end groups is rationally designed and synthesized. FINIC shows similar absorption profile in dilute solution to the nonfluorinated analogue INIC. However, compared with INIC, FINIC film shows red‐shifted absorption, down‐shifted frontier molecular orbital energy levels, enhanced crystallinity, and more ordered molecular packing. Single‐crystal structure data show that FINIC molecules pack into closer 3D “network” motif through H‐bonding and π–π interaction, while INIC molecules pack into incompact “honeycomb” motif through only π–π stacking. Theoretical calculations reveal that FINIC has stronger electronic coupling and more molecular interactions than INIC. FINIC has higher electron mobilities in both horizontal and vertical directions than INIC. Moreover, FINIC and INIC support efficient 3D exciton transport. PBD‐SF/FINIC blend has a larger driving force for exciton splitting, more efficient charge transfer and photoinduced charge generation. Finally, the organic solar cells based on PBD‐SF/FINIC blend yield power conversion efficiency of 14.0%, far exceeding that of the PBD‐SF/INIC‐based devices (5.1%).

Dai, Shuixing↗

Rational Control of Off‐State Heterogeneity in a Photoswitchable Fluorescent Protein Provides Switching Contrast Enhancement**

Abstract Reversibly photoswitchable fluorescent proteins are essential markers for advanced biological imaging, and optimization of their photophysical properties underlies improved performance and novel applications. Here we establish a link between photoswitching contrast, one of the key parameters that dictate the achievable resolution in nanoscopy applications, and chromophore conformation in the non‐fluorescent state of rsEGFP2, a widely employed label in REversible Saturable OpticaL Fluorescence Transitions (RESOLFT) microscopy. Upon illumination, the cis chromophore of rsEGFP2 isomerizes to two distinct off ‐state conformations, trans1 and trans2 , located on either side of the V151 side chain. Reducing or enlarging the side chain at this position (V151A and V151L variants) leads to single off ‐state conformations that exhibit higher and lower switching contrast, respectively, compared to the rsEGFP2 parent. The combination of structural information obtained by serial femtosecond crystallography with high‐level quantum chemical calculations and with spectroscopic and photophysical data determined in vitro suggests that the changes in switching contrast arise from blue‐ and red‐shifts of the absorption bands associated to trans1 and trans2 , respectively. Thus, due to elimination of trans2 , the V151A variants of rsEGFP2 and its superfolding variant rsFolder2 display a more than two‐fold higher switching contrast than their respective parent proteins, both in vitro and in E. coli cells. The application of the rsFolder2‐V151A variant is demonstrated in RESOLFT nanoscopy. Our study rationalizes the connection between structural and photophysical chromophore properties and suggests a means to rationally improve fluorescent proteins for nanoscopy applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Biased tracers in redshift space in the EFTofLSS with exact time dependence

We study the effect of the Einstein-de Sitter (EdS) approximation on the one-loop power spectrum of galaxies in redshift space in the Effective Field Theory of Large-Scale Structure. The dark matter density perturbations and velocity divergence are treated with exact time dependence. Splitting the density perturbation into its different temporal evolutions naturally gives rise to an irreducible basis of biases. While, as in the EdS approximation, at each time this basis spans a seven-dimensional space, this space is a slightly different one, and the difference is captured by a single calculable time- and vec k-dependent function. We then compute the redshift-space galaxy one-loop power spectrum with the EdS approximation (P EdS-approx ) and without (P Exact ). For the monopole we find P 0 Exact /P 0 Ed S-approx ~ 1.003 and for the quadrupole P 2 Exact /P 2 EdS-approx ~ 1.007 at z=0.57, and sharply increasing at lower redshifts. Finally, we show that a substantial fraction of the effect remains even after allowing the bias coefficients to shift within a physically allowed range. This suggests that the EdS approximation can only fit the data to a level of precision that is roughly comparable to the precision of the next generation of cosmological surveys. Furthermore, we find that implementing the exact time dependence formalism is not demanding and is easily applicable to data. Both of these points motivate a direct study of this effect on the cosmological parameters.

79 ASTRONOMY AND ASTROPHYSICS↗

A measurement of stellar surface gravity hidden in radial velocity differences of comoving stars

The gravitational redshift induced by stellar surface gravity is notoriously difficult to measure for non-degenerate stars, since its amplitude is small in comparison with the typical Doppler shift induced by stellar radial velocity. In this study, we make use of the large observational data set of the Gaia mission to achieve a significant reduction of noise caused by these random stellar motions. By measuring the differences in velocities between the components of the pairs of comoving stars and wide binaries, in this work we are able to statistically measure the combined effects of gravitational redshift and convective blueshifting of spectral lines, and nullify the effect of the peculiar motions of the stars. For the subset of stars considered in this study, we find a positive correlation between the observed differences in Gaia radial velocities and the differences in surface gravity and convective blueshift inferred from effective temperature and luminosity measurements. The results rule out a null signal at the 5σ level for our full data set. Additionally, we study the subdominant effects of binary motion, and possible systematic errors in radial velocity measurements within Gaia. Results from the technique presented in this study are expected to improve significantly with data from the next Gaia data release. Such improvements could be used to constrain the mass–luminosity relation and stellar models that predict the magnitude of convective blueshift.

(stars:) binaries: general↗

First Measurement of Z Opacity Sample Evolution near Solar Interior Conditions Using Time-Resolved Spectroscopy

Opacity model differences with Fe opacity measurements at high temperature (T>1⁢8⁢0 eV ) and high electron density (𝑛 𝑒 >3×10 22 cm −3 ) at nearly solar interior conditions have remained unresolved [Bailey et al., Nature 517, 56 (2015) and Nagayama et al., Phys. Rev. Lett. 122, 235001 (2019)]. Systematic errors from temporal gradients are one hypothesis for the discrepancy. Past data recorded on x-ray film provided spectral measurements over a time determined by the 2.8-ns backlighter duration. Here, we present the first measurements of opacity sample temporal evolution using novel hCMOS ultrafast x-ray camera technology. The measured conditions, measured backlighter time history, and modeled opacities are used to show that temporal gradients do not resolve the model-data discrepancy. The methods demonstrated provide potential advantages, including opacities at more extreme conditions, spectral line shift measurements, and improved measurements at other facilities.

atomic spectra↗

Lipo-Chitooligosaccharides Induce Specialized Fungal Metabolite Profiles That Modulate Bacterial Growth

Lipo-chitooligosaccharides (LCOs) are historically known for their role as microbial-derived signaling molecules that shape plant symbiosis with beneficial rhizobia or mycorrhizal fungi. Recent studies showing that LCOs are widespread across the fungal kingdom have raised questions about the ecological function of these compounds in organisms that do not form symbiotic relationships with plants. To elucidate the ecological function of these compounds, we investigate the metabolomic response of the ubiquitous human pathogen Aspergillus fumigatus to LCOs. Our metabolomics data revealed that exogenous application of various types of LCOs to A. fumigatus resulted in significant shifts in the fungal metabolic profile, with marked changes in the production of specialized metabolites known to mediate ecological interactions. Using network analyses, we identify specific types of LCOs with the most significant effect on the abundance of known metabolites. Extracts of several LCO-induced metabolic profiles significantly impact the growth rates of diverse bacterial species. These findings suggest that LCOs may play an important role in the competitive dynamics of non-plant-symbiotic fungi and bacteria. This study identifies specific metabolomic profiles induced by these ubiquitously produced chemicals and creates a foundation for future studies into the potential roles of LCOs as modulators of interkingdom competition.

59 BASIC BIOLOGICAL SCIENCES↗

High-Fidelity Numerical Wave Tank Verification & Validation Study: Wave Generation Through Paddle Motion: Preprint

This paper presents a numerical benchmark study of wave propagation due to a paddle motion using different high-fidelity numerical models, which are capable of replicating the nearly actual physical wave tank testing. A full time series of the measured wave generation paddle motion which was used to generate wave propagation in the physical wave tank will be utilized in each of the models contributed by IEA OES Task 10's participants, which includes both computational fluid dynamics (CFD) and smooth hydrodynamic particle (SPH). The high-fidelity simulations of the physical wave testcase will allow for the evaluation of the initial transient effects from wave ramp-up and its evolution in the wave tank over time for two representative regular waves with varying levels of nonlinearity. A couple of interesting metrics like the predicted wave surface elevation at select wave probes, wave period, and phase-shift in time will be assessed to evaluate the relative accuracy of numerical models versus experimental data within specified time intervals. These models will serve as a guide for modelers in the wave energy community and provide a base case to allow further and more detailed numerical modeling of the fixed Kramer Sphere Cases under wave excitation force wave tank testing.

HYDRO ENERGY,TIDAL AND WAVE POWER↗

Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA) (Final Scientific/Technical Report, Subcontract Award No. 6953691)

The overarching objective the Baliga lab is to develop a mechanistic understanding of the field relevant metabolic processes underlying community partitioning of respiration pathways and physiological state shifts. In order to dissect how environmental perturbations influence microbial regulatory networks, we have constructed data-driven models that capture the dynamic interplay between abiotic and biotic factors during laboratory simulations. Specifically, we have developed multiple computational models (i.e., cMonkey2, EGRIN 2.0, and multiple genome-scale metabolic models) connecting environmental influence to genome-encoded regulatory programs. In addition, we have generated new tools such as Live Anaerobic Cell Sorting (LAnCS) and Fluidized Bed Reactors (FBRs) to investigate the dynamics of community partitioning of metabolic processes supporting sulfate or nitrate respiration, which are two critical activities observed from the field site at Oak Ridge National Labs (ORNL). This framework of connecting field phenomena with laboratory simulations and computational modeling has created the Environmental Simulations and Modelling (EnvSim) campaign within ENIGMA. The campaign, which spans multiple laboratories across ENIGMA and is led by the Baliga group, has established synthetic communities (SynComs) to discover, characterize, and dissect key microbial processes relevant to field observations, such as emissions of the greenhouse gas nitrous oxide (N 2 O). For example, the EnvSim campaign so far has deduced four potential mechanisms that may account for the N 2 O emissions at the FRC and are currently being investigated by labs across ENIGMA. Denitrification may be driven by complete denitrifiers, however, their NosZ enzymes, which catalyze the final step in denitrification by converting N 2 O to N 2 , may be sensitive at a lower pH. Additionally, the denitrification process could be partitioned among organisms and some may have pH-sensitive NosZ genes. Another factor contributing to variable N 2 O emissions relates to the metal co-factors involved in the denitrification pathway. For instance, excess Cu, Al, Mn, U, Ni, Co, Cu, and/or Cd may have inhibitory effects on multiple enzymatic steps during denitrification, while the enzymatic production of nitrite, the precursor of N 2 O, can be limited by the essential metal Mo. Lastly, abiotic production of N 2 O may occur as a result of chemodenitrification, in which metals like Fe, Mn, and some organic compounds can drive redox reactions that convert nitrogen cycle intermediates to N 2 O under the right conditions. Current investigations and analyses have generated multiple transcriptomic profiles aiding the refinement of new metabolic and gene regulatory network model for our denitrifying SynCom. In sum, the previous funding cycle has generated 33 peer-reviewed publications spanning predictive network biology to co-operativity of mutualistic interspecies interactions of evolved communities. Our integration of multiple omics datasets and the development of new network modelling tools has revealed important insights that can explain field observed phenomenon and generated new hypotheses to be investigated in the field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

SCGSR Final Report

Dual phase noble liquid time projection chambers (TPCs) have an unprecedented sensitivity towards dark matter searches and neutrinos, owing to their ability to detect single electrons. When a particle deposits energy into one of these detectors, electrons are released and drifted into a gaseous region of high electric field. In the gas, the electrons are accelerated to produce extra light into what is called an S2 pulse. This light is far easier to detect than the current induced from a single electron. The two widely used elements for noble liquid detectors are Xe and Ar. Xe has a larger chance of being hit by neutrinos or dark matter, but ends with a lower (i.e. harder to detect) energy when struck. However, Ar light is hard to reflect and detect due to its short wavelength which is absorbed by most materials. While one may use TPB to re-emit this light at a longer wavelength, spatial variations of TPB thickness can smear the amount of re-emitted light, and thus cause a poor S2 energy resolution and spatial resolution. Furthermore, a single electron pulse in Ar is spread over a much longer time than one in Xe, which makes it harder to identify single electrons in Ar. The goal of Xe doping is to combine the best aspects of Ar with the best aspects of Xe. More specifically, this means a detector with a larger yield of photons and electrons produced per energy deposition, an ability to collect more of the light given by those signals, and a narrower pulse from the electron signal. During this project, we developed and operated a detector that can overcome the thermodynamic challenges of Xe doping – namely, preventing Xe from freezing. In the first experimental run during this award period, we found that we can achieve at least 5% of Xe in LAr, and measure the Xe concentration in the gas. In the second run, we installed our detector and saw the first S2 pulses in a dual phase Xe doped LAr TPC. With the data we took, we quantified the effect of Xe doping on the detectability of wavelength-shifted light produced in a Xe doped Ar mixture, as well as the single electron pulse shape. While the Ar light has successfully been wavelength shifted, we learned that much to our surprise, Xe doping actually makes the S2 pulse wider before it makes it narrower.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

A Modified Model Dielectric Function for Analyzing Optical Spectra of InGaN Nanofilms on Sapphire Substrates

Due to a lower InN bandgap energy Eg~0.7 eV, InxGa1−xN/Sapphire epifilms are considered valuable in the development of low-dimensional heterostructure-based photonic devices. Adjusting the composition x and thickness d in epitaxially grown films has offered many possibilities of light emission across a wide spectral range, from ultraviolet through visible into near-infrared regions. Optical properties have played important roles in making semiconductor materials useful in electro-optic applications. Despite the efforts to grow InxGa1−xN/Sapphire samples, no x- and d-dependent optical studies exist for ultrathin films. Many researchers have used computationally intensive methods to study the electronic band structures Ejk→, and subsequently derive optical properties. By including inter-band transitions at critical points from Ejk→, we have developed a semiempirical approach to comprehend the optical characteristics of InN, GaN and InxGa1−xN. Refractive indices of InxGa1−xN and sapphire substrate are meticulously integrated into a transfer matrix method to simulate d- and x-dependent reflectivity RE and transmission TE spectra of nanostructured InxGa1−xN/Sapphire epifilms. Analyses of RE and TE have offered accurate x-dependent shifts of energy gaps for InxGa1−xN (x = 0.5, 0.7) in excellent agreement with the experimental data.

Talwar, Devki N. (ORCID:0000000202723195)↗

Mapping Post-Climate Change Biogeographical Regions with Deep Latent Variable Models

Forecasting future changes to biodiversity due to shifts in climate is challenging due to nonlinear interactions between species as recorded in their presence/absence data. This work proposes using variational autoencoders with environmental covariates to identify low-dimensional structure in species’ joint co-occurrence patterns and leveraging this simplified representation to provide multivariate predictions of their habitat extent under future climate scenarios. We pursue a latent space clustering approach to map biogeographical regions of frequently co-occurring species and apply this methodology to a dataset from northern Belgium, generating predictive maps illustrating how these regions may expand or contract with changing temperature under a future climate scenario.

Krapu, Chris↗

Detailed Laboratory Evaluation of Electric Demand Load Shifting Potential of Controlled Heat Pump Water Heaters

The demand profile management of electric end uses is vital research for utilities and policymakers planning greenhouse gas emission reductions. In this study, detailed laboratory research was conducted on the load shifting potential of 4 grid-connected HPWHs and one electric resistance water heaters (ERWH). The testing applied different CTA-2045 shed and critical peak command designs under three water draw profiles. Highly-controlled laboratory experiments were conducted in Florida. One of the four HPWHs was a prototype incorporating the new CTA-2045-B protocol feature allowing ‘advanced’ load-up above tank setpoint. A three-hour morning (6 – 9 AM) and four-hour evening curtailment (4 – 8 PM) were defined as the shed or critical peak periods reflecting high-value control periods for utility coincident load for system-wide electric demand reductions. Tests were performed under baseline conditions (no load shift) and under varied load-shifting schemes, including load up and advanced load up, ahead of shed and critical peak commands. Data were collected from December 2020 – February 2022 in the laboratory and compared with field experiments in Florida and the Pacific Northwest. Grid-connected HPWHs were found to reduce peak demand by up to 0.47 kW compared to uncontrolled HPWH units, depending on time of day, control scheme, draw profile, and temperature cluster. The load up strategy demonstrated the ability of all units to utilize heat pump mode for extended periods ahead of peak events. Demand reductions for the HPWHs were much larger when compared with the ERWH— up to 1.64 kW with large hot water draws in winter.

Fenaughty, Karen↗

The FIREBIRD-II CubeSat Mission (Focused Investigations of Relativistic Electron Burst Intensity, Range, and Dynamics)

FIREBIRD-II is a National Science Foundation funded CubeSat mission designed to study the scale size and energy spectrum of relativistic electron microbursts. The mission consists of two identical 1.5 U CubeSats in a low earth polar orbit, each with two solid state detectors that differ only in the size of their geometric factors and fields of view. Having two spacecraft in close orbit allows the scale size of microbursts to be investigated through the intra-spacecraft separation when microbursts are observed simultaneously on each unit. Each detector returns high cadence (10 s of ms) measurements of the electron population from 200 keV to >1 MeV across six energy channels. The energy channels were selected to fill a gap in the observations of the Heavy Ion Large Telescope instrument on the Solar, Anomalous, and Magnetospheric Particle Explorer. FIREBIRD-II has been in orbit for 5 years and continues to return high quality data. After the first month in orbit, the spacecraft had separated beyond the expected scale size of microbursts, so the focus has shifted toward conjunctions with other magnetospheric missions. FIREBIRD-II has addressed all of its primary science objectives, and its long lifetime and focus on conjunctions has enabled additional science beyond the scope of the original mission. This paper presents a brief history of the FIREBIRD mission’s science goals, followed by a description of the instrument and spacecraft. The data products are then discussed along with some caveats necessary for proper use of the data.

47 OTHER INSTRUMENTATION↗

New Norms or Old Habits: Evaluating Interlinked Trajectories of Online Shopping and Work Commute Post-Pandemic

The COVID-19 pandemic has significantly shifted travel behaviors, with major changes observed in online shopping and travel to work. Despite considerable research into pandemic-induced changes in travel behavior, it remains uncertain whether these new patterns have persisted or reverted to pre-pandemic norms. This study addresses this uncertainty by evaluating whether shifts in online shopping and work travel during the pandemic have become permanently ingrained in individuals' daily routine. Leveraging data from the 2022 National Household Travel Survey, a bivariate ordered probit model is employed to analyze changes in online shopping and work travel - whether they have increased, decreased, or remained stable compared to pre-pandemic levels across different population segments. The analysis finds that the pandemic did not significantly alter online shopping for home delivery and travel to work for the majority of society. However, a substantial portion of respondents reported increased online shopping for home delivery and reduced travel to work compared to pre-pandemic levels, with online shopping trends appearing more permanent. Segment-wise analysis and model results indicate heterogeneity in behavioral shifts with females engaging more in online shopping, while zero-vehicle households are traveling less to work, compared to pre-pandemic levels. Additionally, increase in online shopping frequency is significantly and negatively correlated with decrease in traveling to work. These findings highlight the need for improved digital infrastructure, flexible work policies, and integrated transportation solutions tailored to evolving demographic and socioeconomic needs in the post-pandemic era. Additionally, the study calls for integrating passenger and freight movement in a single framework rather than treating them in silos.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Alcock-Paczynski Blinding Scheme for the Ly-$α$ Forest Analysis

We present and validate a blinding method for the Lyman-$α$ (Ly$α$) forest analysis based on a modification of the Alcock-Paczynski test. In order to hide the background expansion history, the method employs a geometrical shift of each quasar (QSO) forest in wavelength space, once the quasar continuum has been fitted and the fluctuation field is extracted. The redshift positions for the QSO sample are also changed in a consistent manner. We show that the method remains effective when applied to real data, where contamination from metals and Lyman-$β$ is intrinsically mixed with the Lyman-$α$ forest. This limitation is primarily visible in the 1D correlation function, where other blinding strategies can mitigate the effect. To assess its effectiveness, the prescription is tested against a series of datasets of increasing complexity: from idealized low-noise mocks, to realistic DESI year one synthetic datasets, and finally to data from DESI first data release (DR1), using both the auto (Ly$α\times$Ly$α$) and cross (Ly$α\times$ QSO) correlations. We find that the method robustly shifts the BAO peak position from the 3D correlation functions to the expected value for cosmology changes of around 5% in the matter content, without altering the shape of the posteriors in the model parameters. In conclusion, this catalog-level blinding strategy is a viable method for cosmological inference with the Lyman-$α$ forest, particularly if a cross-analysis with other tracers using the same blinding strategy is pursued.

Perez-Sanchez, G. [Guanajuato U.] (ORCID:000900096↗

Machine Learning of All Mycobacterium tuberculosis H37Rv RNA-seq Data Reveals a Structured Interplay between Metabolism, Stress Response, and Infection

Mycobacterium tuberculosis is one of the most consequential human bacterial pathogens, posing a serious challenge to 21st century medicine. A key feature of its pathogenicity is its ability to adapt its transcriptional response to environmental stresses through its transcriptional regulatory network (TRN). While many studies have sought to characterize specific portions of the M. tuberculosis TRN, and some studies have performed system-level analysis, few have been able to provide a network-based model of the TRN that also provides the relative shifts in transcriptional regulator activity triggered by changing environments. Here, we compiled a compendium of nearly 650 publicly available, high quality M. tuberculosis RNA-sequencing data sets and applied an unsupervised machine learning method to obtain a quantitative, top-down TRN. It consists of 80 independently modulated gene sets known as “iModulons,” 41 of which correspond to known regulons. These iModulons explain 61% of the variance in the organism’s transcriptional response. We show that iModulons (i) reveal the function of poorly characterized regulons, (ii) describe the transcriptional shifts that occur during environmental changes such as shifting carbon sources, oxidative stress, and infection events, and (iii) identify intrinsic clusters of regulons that link several important metabolic systems, including lipid, cholesterol, and sulfur metabolism. This transcriptome-wide analysis of the M. tuberculosis TRN informs future research on effective ways to study and manipulate its transcriptional regulation and presents a knowledge-enhanced database of all published high-quality RNA-seq data for this organism to date.

59 BASIC BIOLOGICAL SCIENCES↗