Publications

Kidney Precision Medicine Project (KPMP) is building a spatially specified human kidney tissue atlas in health and disease with single-cell resolution. Here, we describe the construction of an integrated reference map of cells, pathways, and genes using unaffected regions of nephrectomy tissues and undiseased human biopsies from 56 adult subjects. We use single-cell/nucleus transcriptomics, subsegmental laser microdissection transcriptomics and proteomics, near-single-cell proteomics, 3D and CODEX imaging, and spatial metabolomics to hierarchically identify genes, pathways, and cells. Integrated data from these different technologies coherently identify cell types/subtypes within different nephron segments and the interstitium. These profiles describe cell-level functional organization of the kidney following its physiological functions and link cell subtypes to genes, proteins, metabolites, and pathways. They further show that messenger RNA levels along the nephron are congruent with the subsegmental physiological activity. This reference atlas provides a framework for the classification of kidney disease when multiple molecular mechanisms underlie convergent clinical phenotypes.

In this paper, we introduce a randomized iterative method for signal detection in uplink large-scale multiple-input multiple-output (MIMO) systems, which not only achieves a low computational complexity but also enjoys a global and exponentially fast convergence. First of all, by adopting the random sampling into the iterations, the randomized iterative detection algorithm (RIDA) is proposed for large-scale MIMO systems. We show that RIDA converges exponentially fast in terms of mean squared error (MSE). Furthermore, this global convergence always holds, and does not depend on the standard requirements such as N≫K , where N and K denote the numbers of antennas at the sides of base station and users. This broadly extends the applications of low-complexity detection in uplink large-scale MIMO systems. Then, based on a new conditional sampling, optimization and enhancements are given to further improve both the convergence and efficiency of RIDA, resulting in the modified randomized iterative detection algorithm (MRIDA). Meanwhile, with respect to MRIDA, further complexity reduction by exploiting the matrix structure is given while its implementation by deep neural networks (DNN) is also presented for a better detection performance.

Chemical abundances of stars in the Milky Way disk are empirical tracers of its enrichment history. However, they capture joint-information that is valuable to disentangle. In this work, we seek to quantify how individual abundances evolve across the present-day radius of the disk, at fixed supernovae contribution ([Fe/H], [Mg/Fe]). We use 18,135 APOGEE DR17 red clump stars and 7,943 GALAH DR3 main sequence stars to compare the abundance distributions conditioned on ([Fe/H], [Mg/Fe]) across 3−13 kpc and 6.5−9.5 kpc, respectively. In total we examine 15 elements: C, N, Al, K (light), O, Si, S, Ca, (α), Mn, Ni, Cr, Cu, (iron-peak) Ce, Ba (s-process) and Eu (r-process). We find that the conditional neutron capture and light elements most significantly trace variations in the disk's enrichment history, with absolute conditional radial gradients ≤0.03 dex/kpc. The other elements studied have absolute conditional gradients ≲0.01 dex/kpc. We uncover structured conditional abundance variations as a function of [Fe/H] for the low-α, but not the high-α sequence. The average scatter between the mean conditional abundances at different radii is σintrinsic≈ 0.02 dex (with Ce, Eu, Ba σintrinsic> 0.05 dex). These results serve as a measure of the magnitude via which different elements trace Galactic radial enrichment history once fiducial supernovae correlations are accounted for. Furthermore, we uncover subtle systematic variations in all moments of the conditional abundance distributions that will presumably constrain chemical evolution models of the Galaxy.

Extended, old, and round stellar halos appear to be ubiquitous around high-mass dwarf galaxies (108.5 < M⋆/M⊙ < 109.6) in the observed universe. However, it is unlikely that these dwarfs have undergone a sufficient number of minor mergers to form stellar halos that are composed of predominantly accreted stars. Here, we demonstrate that FIRE-2 (Feedback in Realistic Environments) cosmological zoom-in simulations are capable of producing dwarf galaxies with realistic structures, including both a thick disk and round stellar halo. Crucially, these stellar halos are formed in situ, largely via the outward migration of disk stars. However, there also exists a large population of "nondisky" dwarfs in FIRE-2 that lack a well-defined disk/halo and do not resemble the observed dwarf population. These nondisky dwarfs tend to be either more gas-poor or to have burstier recent star formation histories than the disky dwarfs, suggesting that star formation feedback may be preventing disk formation. Both classes of dwarfs underscore the power of a galaxy's intrinsic shape—which is a direct quantification of the distribution of the galaxy's stellar content—to interrogate the feedback implementation in simulated galaxies.

We derive efficient, closed-form, differentiable, and numerically stable solutions for the flux measured from a spherical planet or moon seen in reflected light, either in or out of occultation. Our expressions apply to the computation of scattered light phase curves of exoplanets, secondary eclipse light) curves in the optical, or future measurements of planet–moon and planet–planet occultations, as well as to photometry of solar system bodies. We derive our solutions for Lambertian bodies illuminated by a point source, but extend them to model illumination sources of finite angular size and rough surfaces with phase-dependent scattering. Our algorithm is implemented in Python within the open-source starry mapping framework and is designed with efficient gradient-based inference in mind. The algorithm is ∼4–5 orders of magnitude faster than direct numerical evaluation methods and ∼10 orders of magnitude more precise. We show how the techniques developed here may one day lead to the construction of two-dimensional maps of terrestrial planet surfaces, potentially enabling the detection of continents and oceans on exoplanets in the habitable zone.

We present the first results from an all-sky all-frequency (ASAF) search for an anisotropic stochastic gravitational-wave background using the data from the first three observing runs of the Advanced LIGO and Advanced Virgo detectors. Upper limit maps on broadband anisotropies of a persistent stochastic background were published for all observing runs of the LIGO-Virgo detectors. However, a broadband analysis is likely to miss narrowband signals as the signal-to-noise ratio of a narrowband signal can be significantly reduced when combined with detector output from other frequencies. Data folding and the computationally efficient analysis pipeline, {\tt PyStoch}, enable us to perform the radiometer map-making at every frequency bin. We perform the search at 3072 {\tt{HEALPix}} equal area pixels uniformly tiling the sky and in every frequency bin of width 1/32~Hz in the range 20−1726~Hz, except for bins that are likely to contain instrumental artefacts and hence are notched. We do not find any statistically significant evidence for the existence of narrowband gravitational-wave signals in the analyzed frequency bins. Therefore, we place 95% confidence upper limits on the gravitational-wave strain for each pixel-frequency pair, the limits are in the range (0.030−9.6)×10−24. In addition, we outline a method to identify candidate pixel-frequency pairs that could be followed up by a more sensitive (and potentially computationally expensive) search, e.g., a matched-filtering-based analysis, to look for fainter nearly monochromatic coherent signals. The ASAF analysis is inherently independent of models describing any spectral or spatial distribution of power. We demonstrate that the ASAF results can be appropriately combined over frequencies and sky directions to successfully recover the broadband directional and isotropic results.

The dynamics of the inner Galaxy contain crucial clues for untangling the evolutionary history of the Milky Way. However, the inner Galaxy's gravitational potential is poorly constrained, partly because the length of the Galactic bar is currently under debate with length estimates ranging from 3.5-5 kpc. We present a novel method for constraining the length and pattern speed of the Galactic bar using 6D phase space information to directly integrate orbits. We verify our method with N-body simulations and find that the maximal extent of orbits in the bar is not always consistent with that of the potential used to calculate the orbits. It is only consistent when the length of the bar in said potential is similar to the N-body model from which the initial positions and velocities of the stars are sampled. When we apply the orbit integration method to ≈210,000 stars in APOGEE DR17 and Gaia eDR3 data, we find a self-consistent result only for potential models with a dynamical bar length of ≈3.5 kpc and pattern speed of 39 km/s/kpc. We find the Milky Way's trapped bar orbits extend out to only ≈3.5 kpc, but there is also an overdensity of stars at the end of the bar out to 4.8 kpc which could be related to an attached spiral arm. We also find that the measured orbital structure of the bar is strongly dependent on the properties of the assumed potential.

Marked power spectra are two-point statistics of a marked field obtained by weighting each location with a function that depends on the local density around that point. We consider marked power spectra of the galaxy field in redshift space that up-weight low density regions, and perform a Fisher matrix analysis to assess the information content of this type of statistics using the Molino mock catalogs built upon the Quijote simulations. We identify four different ways to up-weight the galaxy field, and compare the Fisher information contained in their marked power spectra to the one of the standard galaxy power spectrum, when considering monopole and quadrupole of each statistic. Our results show that each of the four marked power spectra can tighten the standard power spectrum constraints on the cosmological parameters Om, Ob, h, ns, Mν by 15−25 and on s8 by a factor of 2. The same analysis performed by combining the standard and four marked power spectra shows a substantial improvement compared to the power spectrum constraints that is equal to a factor of 6 for σ8 and 2.5−3 for the other parameters. Our constraints may be conservative, since the galaxy number density in the Molino catalogs is much lower than the ones in future galaxy surveys, which will allow them to probe lower density regions of the large-scale structure.

Marked power spectra are two-point statistics of a marked field obtained by weighting each location with a function that depends on the local density around that point. We consider marked power spectra of the galaxy field in redshift space that up-weight low density regions, and perform a Fisher matrix analysis to assess the information content of this type of statistics using the Molino mock catalogs built upon the Quijote simulations. We identify four different ways to up-weight the galaxy field, and compare the Fisher information contained in their marked power spectra to the one of the standard galaxy power spectrum, when considering monopole and quadrupole of each statistic. Our results show that each of the four marked power spectra can tighten the standard power spectrum constraints on the cosmological parameters $$\Omega m, \Omega b, h, n_s, M_ν by 15−25% and on \sigma_8$$ by a factor of 2. The same analysis performed by combining the standard and four marked power spectra shows a substantial improvement compared to the power spectrum constraints that is equal to a factor of 6 for σ8 and 2.5−3 for the other parameters. Our constraints may be conservative, since the galaxy number density in the Molino catalogs is much lower than the ones in future galaxy surveys, which will allow them to probe lower density regions of the large-scale structure.

Although membranous nephropathy (MN) is one of the most common causes of nephrotic syndrome, the molecular characteristics of the kidney damage in MN remain poorly defined. In this study, the authors applied a machine-learning framework to predict diagnosis on the basis of gene expression in microdissected kidney tissue from patients with glomerulonephropathies. They found that MN has a glomerular transcriptional signature that distinguishes it from other glomerulonephropathies and identified a set of MN-specific genes differentially expressed across two independent cohorts and robustly recovered in an additional validation cohort. They also found the MN-specific genes are enriched in targets of transcription factor NF-κB and are predominantly expressed in podocytes. This work provides a molecular snapshot of MN and elucidates transcriptional alterations specific to this disease.