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1. Estimating Generalization Performance Along the Trajectory of Proximal SGD in Robust Regression

Author: Tan, Kai and Bellec, Pierre C.
Subjects: Mathematics - Statistics Theory, Computer Science - Machine Learning, Statistics - Methodology
Abstract: This paper studies the generalization performance of iterates obtained by Gradient Descent (GD), Stochastic Gradient Descent (SGD) and their proximal variants in high-dimensional robust regression problems. The number of features is comparable to the sample size and errors may be heavy-tailed. We introduce estimators that precisely track the generalization error of the iterates along the trajectory of the iterative algorithm. These estimators are provably consistent under suitable conditions. The results are illustrated through several examples, including Huber regression, pseudo-Huber regression, and their penalized variants with non-smooth regularizer. We provide explicit generalization error estimates for iterates generated from GD and SGD, or from proximal SGD in the presence of a non-smooth regularizer. The proposed risk estimates serve as effective proxies for the actual generalization error, allowing us to determine the optimal stopping iteration that minimizes the generalization error. Extensive simulations confirm the effectiveness of the proposed generalization error estimates.
Published: 2024

2. Precise Asymptotics of Bagging Regularized M-estimators

Author: Koriyama, Takuya, Patil, Pratik, Du, Jin-Hong, Tan, Kai, and Bellec, Pierre C.
Subjects: Mathematics - Statistics Theory, Statistics - Machine Learning
Abstract: We characterize the squared prediction risk of ensemble estimators obtained through subagging (subsample bootstrap aggregating) regularized M-estimators and construct a consistent estimator for the risk. Specifically, we consider a heterogeneous collection of $M \ge 1$ regularized M-estimators, each trained with (possibly different) subsample sizes, convex differentiable losses, and convex regularizers. We operate under the proportional asymptotics regime, where the sample size $n$, feature size $p$, and subsample sizes $k_m$ for $m \in [M]$ all diverge with fixed limiting ratios $n/p$ and $k_m/n$. Key to our analysis is a new result on the joint asymptotic behavior of correlations between the estimator and residual errors on overlapping subsamples, governed through a (provably) contractible nonlinear system of equations. Of independent interest, we also establish convergence of trace functionals related to degrees of freedom in the non-ensemble setting (with $M = 1$) along the way, extending previously known cases for square loss and ridge, lasso regularizers. When specialized to homogeneous ensembles trained with a common loss, regularizer, and subsample size, the risk characterization sheds some light on the implicit regularization effect due to the ensemble and subsample sizes $(M,k)$. For any ensemble size $M$, optimally tuning subsample size yields sample-wise monotonic risk. For the full-ensemble estimator (when $M \to \infty$), the optimal subsample size $k^\star$ tends to be in the overparameterized regime $(k^\star \le \min\{n,p\})$, when explicit regularization is vanishing. Finally, joint optimization of subsample size, ensemble size, and regularization can significantly outperform regularizer optimization alone on the full data (without any subagging)., Comment: 72 pages, 14 figures, 6 tables
Published: 2024

3. Uncertainty quantification for iterative algorithms in linear models with application to early stopping

Author: Bellec, Pierre C. and Tan, Kai
Subjects: Statistics - Machine Learning, Computer Science - Machine Learning, Mathematics - Statistics Theory, Statistics - Computation, Statistics - Methodology
Abstract: This paper investigates the iterates $\hbb^1,\dots,\hbb^T$ obtained from iterative algorithms in high-dimensional linear regression problems, in the regime where the feature dimension $p$ is comparable with the sample size $n$, i.e., $p \asymp n$. The analysis and proposed estimators are applicable to Gradient Descent (GD), proximal GD and their accelerated variants such as Fast Iterative Soft-Thresholding (FISTA). The paper proposes novel estimators for the generalization error of the iterate $\hbb^t$ for any fixed iteration $t$ along the trajectory. These estimators are proved to be $\sqrt n$-consistent under Gaussian designs. Applications to early-stopping are provided: when the generalization error of the iterates is a U-shape function of the iteration $t$, the estimates allow to select from the data an iteration $\hat t$ that achieves the smallest generalization error along the trajectory. Additionally, we provide a technique for developing debiasing corrections and valid confidence intervals for the components of the true coefficient vector from the iterate $\hbb^t$ at any finite iteration $t$. Extensive simulations on synthetic data illustrate the theoretical results.
Published: 2024

4. Asymptotics of resampling without replacement in robust and logistic regression

Author: Bellec, Pierre C and Koriyama, Takuya
Subjects: Mathematics - Statistics Theory
Abstract: This paper studies the asymptotics of resampling without replacement in the proportional regime where dimension $p$ and sample size $n$ are of the same order. For a given dataset $(X,y)\in \mathbb{R}^{n\times p}\times \mathbb{R}^n$ and fixed subsample ratio $q\in(0,1)$, the practitioner samples independently of $(X,y)$ iid subsets $I_1,...,I_M$ of $\{1,...,n\}$ of size $q n$ and trains estimators $\hat{\beta}(I_1),...,\hat{\beta}(I_M)$ on the corresponding subsets of rows of $(X, y)$. Understanding the performance of the bagged estimate $\bar{\beta} = \frac1M\sum_{m=1}^M \hat{\beta}(I_1),...,\hat{\beta}(I_M)$, for instance its squared error, requires us to understand correlations between two distinct $\hat{\beta}(I_m)$ and $\hat{\beta}(I_{m'})$ trained on different subsets $I_m$ and $I_{m'}$. In robust linear regression and logistic regression, we characterize the limit in probability of the correlation between two estimates trained on different subsets of the data. The limit is characterized as the unique solution of a simple nonlinear equation. We further provide data-driven estimators that are consistent for estimating this limit. These estimators of the limiting correlation allow us to estimate the squared error of the bagged estimate $\bar{\beta}$, and for instance perform parameter tuning to choose the optimal subsample ratio $q$. As a by-product of the proof argument, we obtain the limiting distribution of the bivariate pair $(x_i^T \hat{\beta}(I_m), x_i^T \hat{\beta}(I_{m'}))$ for observations $i\in I_m\cap I_{m'}$, i.e., for observations used to train both estimates., Comment: 25 pages, 10 figures
Published: 2024

5. Error estimation and adaptive tuning for unregularized robust M-estimator

Author: Bellec, Pierre C. and Koriyama, Takuya
Subjects: Mathematics - Statistics Theory
Abstract: We consider unregularized robust M-estimators for linear models under Gaussian design and heavy-tailed noise, in the proportional asymptotics regime where the sample size n and the number of features p are both increasing such that $p/n \to \gamma\in (0,1)$. An estimator of the out-of-sample error of a robust M-estimator is analysed and proved to be consistent for a large family of loss functions that includes the Huber loss. As an application of this result, we propose an adaptive tuning procedure of the scale parameter $\lambda>0$ of a given loss function $\rho$: choosing$\hat \lambda$ in a given interval $I$ that minimizes the out-of-sample error estimate of the M-estimator constructed with loss $\rho_\lambda(\cdot) = \lambda^2 \rho(\cdot/\lambda)$ leads to the optimal out-of-sample error over $I$. The proof relies on a smoothing argument: the unregularized M-estimation objective function is perturbed, or smoothed, with a Ridge penalty that vanishes as $n\to+\infty$, and show that the unregularized M-estimator of interest inherits properties of its smoothed version., Comment: 33 pages, 10 figures
Published: 2023

6. Existence of solutions to the nonlinear equations characterizing the precise error of M-estimators

Author: Bellec, Pierre C and Koriyama, Takuya
Subjects: Mathematics - Statistics Theory
Abstract: Major progress has been made in the previous decade to characterize the asymptotic behavior of regularized M-estimators in high-dimensional regression problems in the proportional asymptotic regime where the sample size $n$ and the number of features $p$ are increasing simultaneously such that $n/p\to \delta \in(0,\infty)$, using powerful tools such as Approximate Message Passing or the Convex Gaussian Min-Max Theorem (CGMT). The asymptotic error and behavior of the regularized M-estimator is then typically described by a system of nonlinear equations with a few scalar unknowns, and the solution to this system precisely characterize the asymptotic error. Application of the CGMT and related machinery requires the existence of a solution to this low-dimensional system of equations. This paper resolves the question of existence of solution to this low-dimensional system for the case of linear models with independent additive noise, when both the data-fitting loss function and regularization penalty are separable and convex. Such existence result for solution to the nonlinear system were previously known under strong convexity for specific estimators such as the Lasso. The main idea behind this existence result is inspired by an argument developed \cite{montanari2019generalization,celentano2020lasso} in different contexts: By constructing an ad-hoc convex minimization problem in an infinite dimensional Hilbert space, the existence of the Lagrange multiplier for this optimization problem makes it possible to construct explicitly solutions to the low-dimensional system of interest. The conditions under which we derive this existence result exactly correspond to the side of the phase transition where perfect recovery $\hat x= x_0$ fails, so that these conditions are optimal.
Published: 2023

7. Corrected generalized cross-validation for finite ensembles of penalized estimators

Author: Bellec, Pierre C., Du, Jin-Hong, Koriyama, Takuya, Patil, Pratik, and Tan, Kai
Subjects: Mathematics - Statistics Theory, Statistics - Methodology, Statistics - Machine Learning
Abstract: Generalized cross-validation (GCV) is a widely-used method for estimating the squared out-of-sample prediction risk that employs a scalar degrees of freedom adjustment (in a multiplicative sense) to the squared training error. In this paper, we examine the consistency of GCV for estimating the prediction risk of arbitrary ensembles of penalized least-squares estimators. We show that GCV is inconsistent for any finite ensemble of size greater than one. Towards repairing this shortcoming, we identify a correction that involves an additional scalar correction (in an additive sense) based on degrees of freedom adjusted training errors from each ensemble component. The proposed estimator (termed CGCV) maintains the computational advantages of GCV and requires neither sample splitting, model refitting, or out-of-bag risk estimation. The estimator stems from a finer inspection of the ensemble risk decomposition and two intermediate risk estimators for the components in this decomposition. We provide a non-asymptotic analysis of the CGCV and the two intermediate risk estimators for ensembles of convex penalized estimators under Gaussian features and a linear response model. Furthermore, in the special case of ridge regression, we extend the analysis to general feature and response distributions using random matrix theory, which establishes model-free uniform consistency of CGCV., Comment: 91 pages, 34 figures; this version adds general proof outlines (in Sections 4.3 and 5.3), add more experiments with non-Gaussian data (in Sections D and E), relaxes an assumption (in Section A.7), clarifies explanations at several places, and corrects minor typos at several places
Published: 2023

8. Multinomial Logistic Regression: Asymptotic Normality on Null Covariates in High-Dimensions

Author: Tan, Kai and Bellec, Pierre C.
Subjects: Mathematics - Statistics Theory, Statistics - Methodology, Statistics - Machine Learning
Abstract: This paper investigates the asymptotic distribution of the maximum-likelihood estimate (MLE) in multinomial logistic models in the high-dimensional regime where dimension and sample size are of the same order. While classical large-sample theory provides asymptotic normality of the MLE under certain conditions, such classical results are expected to fail in high-dimensions as documented for the binary logistic case in the seminal work of Sur and Cand\`es [2019]. We address this issue in classification problems with 3 or more classes, by developing asymptotic normality and asymptotic chi-square results for the multinomial logistic MLE (also known as cross-entropy minimizer) on null covariates. Our theory leads to a new methodology to test the significance of a given feature. Extensive simulation studies on synthetic data corroborate these asymptotic results and confirm the validity of proposed p-values for testing the significance of a given feature.
Published: 2023

9. How do supernova remnants cool? -- I. Morphology, optical emission lines, and shocks

Author: Makarenko, Ekaterina I., Walch, Stefanie, Clarke, Seamus D., Seifried, Daniel, Naab, Thorsten, Nürnberger, Pierre C., and Rathjen, Tim-Eric
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Astrophysics of Galaxies
Abstract: Supernovae (SNe) inject $\sim 10^{51}$ erg in the interstellar medium, thereby shocking and heating the gas. A substantial fraction of this energy is later lost via radiative cooling. We present a post-processing module for the FLASH code to calculate the cooling radiation from shock-heated gas using collisional excitation data from MAPPINGS V. When applying this tool to a simulated SN remnant (SNR), we find that most energy is emitted in the EUV. However, optical emission lines ($[$O III$]$, $[$N II$]$, $[$S II$]$, H${\alpha}$, H${\beta}$) are usually best observable. Our shock detection scheme shows that [S II] and [N II] emissions arise from the thin shell surrounding the SNR, while [O III], H$\rm \alpha$, and H$\rm \beta$ originate from the volume-filling hot gas inside the SNR bubble. We find that the optical emission lines are affected by the SNR's complex structure and its projection onto the plane of the sky because the escaping line luminosity can be reduced by 10 -- 80\% due to absorption along the line-of-sight. Additionally, the subtraction of contaminating background radiation is required for the correct classification of an SNR on the oxygen or sulphur BPT diagrams. The electron temperature and density obtained from our synthetic observations match well with the simulation but are very sensitive to the assumed metallicity., Comment: 18 pages, 13 figures, accepted for publication in MNRAS
Published: 2023
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10. Les déterminants du consentement à l’impôt en France : Analyse de l’enquête du Conseil des prélèvements obligatoires 2021

Author: Boyer, Pierre C., Ingrand, Thibault, and Strassel, Christophe
Published: 2023

11. The chromatin landscape of healthy and injured cell types in the human kidney

Author: Gisch, Debora L., Brennan, Michelle, Lake, Blue B., Basta, Jeannine, Keller, Mark S., Melo Ferreira, Ricardo, Akilesh, Shreeram, Ghag, Reetika, Lu, Charles, Cheng, Ying-Hua, Collins, Kimberly S., Parikh, Samir V., Rovin, Brad H., Robbins, Lynn, Stout, Lisa, Conklin, Kimberly Y., Diep, Dinh, Zhang, Bo, Knoten, Amanda, Barwinska, Daria, Asghari, Mahla, Sabo, Angela R., Ferkowicz, Michael J., Sutton, Timothy A., Kelly, Katherine J., De Boer, Ian H., Rosas, Sylvia E., Kiryluk, Krzysztof, Hodgin, Jeffrey B., Alakwaa, Fadhl, Winfree, Seth, Jefferson, Nichole, Türkmen, Aydın, Gaut, Joseph P., Gehlenborg, Nils, Phillips, Carrie L., El-Achkar, Tarek M., Dagher, Pierre C., Hato, Takashi, Zhang, Kun, Himmelfarb, Jonathan, Kretzler, Matthias, Mollah, Shamim, Jain, Sanjay, Rauchman, Michael, and Eadon, Michael T.
Published: 2024
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12. APSIM-based modeling approach to understand sorghum production environments in Mali

Author: Diancoumba, Madina, Kholová, Jana, Adam, Myriam, Famanta, Mahamoudou, Clerget, Benoît, Traore, Pierre C. S., Weltzien, Eva, Vacksmann, Michel, McLean, Greg, Hammer, Graeme L., van Oosterom, Erik J., and Vadez, Vincent
Published: 2024
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13. Inflammation primes the murine kidney for recovery by activating AZIN1 adenosine-to-inosine editing

Author: Heruye, Segewkal Hawaze, Myslinski, Jered, Zeng, Chao, Zollman, Amy, Makino, Shinichi, Nanamatsu, Azuma, Mir, Quoseena, Janga, Sarath Chandra, Doud, Emma H., Eadon, Michael T., Maier, Bernhard, Hamada, Michiaki, Tran, Tuan M., Dagher, Pierre C., and Hato, Takashi
Subjects: Medical research, Medicine, Experimental, Adenosine deaminase -- Health aspects, Inflammation -- Physiological aspects, Kidneys -- Health aspects -- Genetic aspects, RNA processing -- Health aspects, Cellular proteins -- Health aspects
Abstract: The progression of kidney disease varies among individuals, but a general methodology to quantify disease timelines is lacking. Particularly challenging is the task of determining the potential for recovery from acute kidney injury following various insults. Here, we report that quantitation of post-transcriptional adenosine-to-inosine (A-to-I) RNA editing offers a distinct genome-wide signature, enabling the delineation of disease trajectories in the kidney. A well-defined murine model of endotoxemia permitted the identification of the origin and extent of A-to-I editing, along with temporally discrete signatures of double-stranded RNA stress and adenosine deaminase isoform switching. We found that A-to-I editing of antizyme inhibitor 1 (AZIN1), a positive regulator of polyamine biosynthesis, serves as a particularly useful temporal landmark during endotoxemia. Our data indicate that AZIN1 A-to-I editing, triggered by preceding inflammation, primes the kidney and activates endogenous recovery mechanisms. By comparing genetically modified human cell lines and mice locked in either A-to-I-edited or uneditable states, we uncovered that AZIN1 A-to-I editing not only enhances polyamine biosynthesis but also engages glycolysis and nicotinamide biosynthesis to drive the recovery phenotype. Our findings implicate that quantifying AZIN1 A-to-I editing could potentially identify individuals who have transitioned to an endogenous recovery phase. This phase would reflect their past inflammation and indicate their potential for future recovery., Introduction The polyamines--namely putrescine, spermidine, and spermine --are involved in a variety of fundamental biological processes, such as transcription, translation, cell growth, differentiation, DNA repair, and aging (1-3). Polyamines are [...]
Published: 2024
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14. Shone’s Complex

Author: Wang, Shuo, Wong, Pierre C., Anderson, Robert H., editor, Backer, Carl L., editor, Berger, Stuart, editor, Blom, Nico A., editor, Holzer, Ralf J., editor, Robinson, Joshua D., editor, and Abdulla, Ra-id, Editor-in-Chief
Published: 2024
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15. Autonomic Modulation Options in Cardiovascular Disease Treatment: Current and Emerging

Author: Vien, Albert, Balaji, Poornima, and Qian, Pierre C.
Published: 2023
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16. Measured temperatures using uninterrupted and interrupted sequences of radiofrequency applications in a phantom gel model: implications for esophageal injury

Author: Steiger, Nathaniel, Qian, Pierre C., Foley, Grace, Bomma, Tarun, Kreidieh, Omar, Whitaker, John, Thurber, Clinton J., Koplan, Bruce A., Tadros, Thomas M., Kapur, Sunil, Zei, Paul C., Tedrow, Usha B., Romero, Jorge, and Sauer, William H.
Published: 2023
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17. Noise Covariance Estimation in Multi-Task High-dimensional Linear Models

Author: Tan, Kai, Romon, Gabriel, and Bellec, Pierre C
Subjects: Mathematics - Statistics Theory, Statistics - Methodology, Statistics - Machine Learning
Abstract: This paper studies the multi-task high-dimensional linear regression models where the noise among different tasks is correlated, in the moderately high dimensional regime where sample size $n$ and dimension $p$ are of the same order. Our goal is to estimate the covariance matrix of the noise random vectors, or equivalently the correlation of the noise variables on any pair of two tasks. Treating the regression coefficients as a nuisance parameter, we leverage the multi-task elastic-net and multi-task lasso estimators to estimate the nuisance. By precisely understanding the bias of the squared residual matrix and by correcting this bias, we develop a novel estimator of the noise covariance that converges in Frobenius norm at the rate $n^{-1/2}$ when the covariates are Gaussian. This novel estimator is efficiently computable. Under suitable conditions, the proposed estimator of the noise covariance attains the same rate of convergence as the "oracle" estimator that knows in advance the regression coefficients of the multi-task model. The Frobenius error bounds obtained in this paper also illustrate the advantage of this new estimator compared to a method-of-moments estimator that does not attempt to estimate the nuisance. As a byproduct of our techniques, we obtain an estimate of the generalization error of the multi-task elastic-net and multi-task lasso estimators. Extensive simulation studies are carried out to illustrate the numerical performance of the proposed method.
Published: 2022

18. Long‐Term Safety and Efficacy of Transcatheter Microwave and Radiofrequency Denervation in a Chronic Ovine Model

Author: Poornima Balaji, Michael A. Barry, Vu Toan Tran, Simone Marschner, Juntang Lu, Duc Minh Nguyen, Ashraf Mina, Sushil Bandodkar, Shirley Alvarez, Virginia James, John Ronquillo, Winny Varikatt, Pramesh Kovoor, Alistair McEwan, Aravinda Thiagalingam, Stuart P. Thomas, and Pierre C. Qian
Subjects: blood pressure, microwave, radiofrequency, renal denervation, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract: Background Transcatheter renal denervation (RDN) has had inconsistent efficacy and concerns for durability of denervation. We aimed to investigate long‐term safety and efficacy of transcatheter microwave RDN in vivo in normotensive sheep in comparison to conventional radiofrequency ablation. Methods and Results Sheep underwent bilateral RDN, receiving 1 to 2 microwave ablations (maximum power of 80–120 W for 240 s–480 s) and 12 to 16 radiofrequency ablations (180 s–240 s) in the main renal artery in a paired fashion, alternating the side of treatment, euthanized at 2 weeks (acute N=15) or 5.5 months (chronic N=15), and compared with undenervated controls (N=4). Microwave RDN produced substantial circumferential perivascular injury compared with radiofrequency at both 2 weeks [area 239.8 (interquartile range [IQR] 152.0–343.4) mm2 versus 50.1 (IQR, 32.0–74.6) mm2, P
Published: 2024
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19. Observable adjustments in single-index models for regularized M-estimators

Author: Bellec, Pierre C
Subjects: Mathematics - Statistics Theory, Statistics - Machine Learning
Abstract: We consider observations $(X,y)$ from single index models with unknown link function, Gaussian covariates and a regularized M-estimator $\hat\beta$ constructed from convex loss function and regularizer. In the regime where sample size $n$ and dimension $p$ are both increasing such that $p/n$ has a finite limit, the behavior of the empirical distribution of $\hat\beta$ and the predicted values $X\hat\beta$ has been previously characterized in a number of models: The empirical distributions are known to converge to proximal operators of the loss and penalty in a related Gaussian sequence model, which captures the interplay between ratio $p/n$, loss, regularization and the data generating process. This connection between$(\hat\beta,X\hat\beta)$ and the corresponding proximal operators require solving fixed-point equations that typically involve unobservable quantities such as the prior distribution on the index or the link function. This paper develops a different theory to describe the empirical distribution of $\hat\beta$ and $X\hat\beta$: Approximations of $(\hat\beta,X\hat\beta)$ in terms of proximal operators are provided that only involve observable adjustments. These proposed observable adjustments are data-driven, e.g., do not require prior knowledge of the index or the link function. These new adjustments yield confidence intervals for individual components of the index, as well as estimators of the correlation of $\hat\beta$ with the index. The interplay between loss, regularization and the model is thus captured in a data-driven manner, without solving the fixed-point equations studied in previous works. The results apply to both strongly convex regularizers and unregularized M-estimation. Simulations are provided for the square and logistic loss in single index models including logistic regression and 1-bit compressed sensing with 20\% corrupted bits.
Published: 2022

20. Planet’s Biomass Proxy for monitoring aboveground agricultural biomass and estimating crop yield

Author: Guillevic, Pierre C., Aouizerats, Benjamin, Burger, Rogier, Den Besten, Nadja, Jackson, Daniel, Ridderikhoff, Margot, Zajdband, Ariel, Houborg, Rasmus, Franz, Trenton E., Robertson, G. Philip, and De Jeu, Richard
Published: 2024
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21. The chromatin landscape of healthy and injured cell types in the human kidney

Author: Debora L. Gisch, Michelle Brennan, Blue B. Lake, Jeannine Basta, Mark S. Keller, Ricardo Melo Ferreira, Shreeram Akilesh, Reetika Ghag, Charles Lu, Ying-Hua Cheng, Kimberly S. Collins, Samir V. Parikh, Brad H. Rovin, Lynn Robbins, Lisa Stout, Kimberly Y. Conklin, Dinh Diep, Bo Zhang, Amanda Knoten, Daria Barwinska, Mahla Asghari, Angela R. Sabo, Michael J. Ferkowicz, Timothy A. Sutton, Katherine J. Kelly, Ian H. De Boer, Sylvia E. Rosas, Krzysztof Kiryluk, Jeffrey B. Hodgin, Fadhl Alakwaa, Seth Winfree, Nichole Jefferson, Aydın Türkmen, Joseph P. Gaut, Nils Gehlenborg, Carrie L. Phillips, Tarek M. El-Achkar, Pierre C. Dagher, Takashi Hato, Kun Zhang, Jonathan Himmelfarb, Matthias Kretzler, Shamim Mollah, the Kidney Precision Medicine Project (KPMP), Sanjay Jain, Michael Rauchman, and Michael T. Eadon
Subjects: Science
Abstract: Abstract There is a need to define regions of gene activation or repression that control human kidney cells in states of health, injury, and repair to understand the molecular pathogenesis of kidney disease and design therapeutic strategies. Comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measure dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and H3K27ac, H3K4me1, H3K4me3, and H3K27me3 histone modifications to decipher the chromatin landscape and gene regulation of the kidney in reference and adaptive injury states. We establish a spatially-anchored epigenomic atlas to define the kidney’s active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we note distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3, KLF6, and KLF10 regulates the transition between health and injury, while in thick ascending limb cells this transition is regulated by NR2F1. Further, combined perturbation of ELF3, KLF6, and KLF10 distinguishes two adaptive proximal tubular cell subtypes, one of which manifested a repair trajectory after knockout. This atlas will serve as a foundation to facilitate targeted cell-specific therapeutics by reprogramming gene regulatory networks.
Published: 2024
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22. Enhancing Alfalfa Biomass Prediction: An Innovative Framework Using Remote Sensing Data

Author: Matias F. Lucero, Carlos M. Hernández, Ana J. P. Carcedo, Ariel Zajdband, Pierre C. Guillevic, Rasmus Houborg, Kevin Hamilton, and Ignacio A. Ciampitti
Subjects: alfalfa, remote sensing, biomass prediction, framework, BART, PlanetScope, Science
Abstract: Estimating pasture biomass has emerged as a promising avenue to assist farmers in identifying the best cutting times for maximizing biomass yield using satellite data. This study aims to develop an innovative framework integrating field and satellite data to estimate aboveground biomass in alfalfa (Medicago sativa L.) at farm scale. For this purpose, samples were collected throughout the 2022 growing season on different mowing dates at three fields in Kansas, USA. The satellite data employed comprised four sources: Sentinel-2, PlanetScope, Planet Fusion, and Biomass Proxy. A grid of hyperparameters was created to establish different combinations and select the best coefficients. The permutation feature importance technique revealed that the Planet’s PlanetScope near-infrared (NIR) band and the Biomass Proxy product were the predictive features with the highest contribution to the biomass prediction model’s. A Bayesian Additive Regression Tree (BART) was applied to explore its ability to build a predictive model. Its performance was assessed via statistical metrics (r2: 0.61; RMSE: 0.29 kg.m−2). Additionally, uncertainty quantifications were proposed with this framework to assess the range of error in the predictions. In conclusion, this integration in a nonparametric approach achieved a useful predicting tool with the potential to optimize farmers’ management decisions.
Published: 2024
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23. A reference tissue atlas for the human kidney

Author: Hansen, Jens, Sealfon, Rachel, Menon, Rajasree, Eadon, Michael T, Lake, Blue B, Steck, Becky, Anjani, Kavya, Parikh, Samir, Sigdel, Tara K, Zhang, Guanshi, Velickovic, Dusan, Barwinska, Daria, Alexandrov, Theodore, Dobi, Dejan, Rashmi, Priyanka, Otto, Edgar A, Rivera, Miguel, Rose, Michael P, Anderton, Christopher R, Shapiro, John P, Pamreddy, Annapurna, Winfree, Seth, Xiong, Yuguang, He, Yongqun, de Boer, Ian H, Hodgin, Jeffrey B, Barisoni, Laura, Naik, Abhijit S, Sharma, Kumar, Sarwal, Minnie M, Zhang, Kun, Himmelfarb, Jonathan, Rovin, Brad, El-Achkar, Tarek M, Laszik, Zoltan, He, John Cijiang, Dagher, Pierre C, Valerius, M Todd, Jain, Sanjay, Satlin, Lisa M, Troyanskaya, Olga G, Kretzler, Matthias, Iyengar, Ravi, Azeloglu, Evren U, and Project, Kidney Precision Medicine
Subjects: Biotechnology, Genetics, Kidney Disease, Aetiology, 2.1 Biological and endogenous factors, Underpinning research, 1.1 Normal biological development and functioning, Generic health relevance, Renal and urogenital, Good Health and Well Being, Humans, Kidney, Kidney Diseases, Metabolomics, Proteomics, Transcriptome, Kidney Precision Medicine Project
Abstract: 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.
Published: 2022

24. Impact of Relative Volume Difference Between Magnetic Resonance Imaging and Three-dimensional Transrectal Ultrasound Segmentation on Clinically Significant Prostate Cancer Detection in Fusion Magnetic Resonance Imaging–targeted Biopsy

Author: Lenfant, Louis, Beitone, Clément, Troccaz, Jocelyne, Beaugerie, Aurélien, Rouprêt, Morgan, Seisen, Thomas, Renard-Penna, Raphaele, Voros, Sandrine, and Mozer, Pierre C.
Published: 2024
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25. Stacking nitrogen mitigation strategies for future pasture-based dairy farms: impacts on leaching and profit

Author: Pierre C. Beukes, Craig Depree, Katrina A. Macintosh, and David Silva-Villacorta
Subjects: modelling, N input, off-paddock infrastructure, recycling N, maize block, salt, Veterinary medicine, SF600-1100
Abstract: Producers in New Zealand’s pasture-based, seasonal dairy sector are striving to reduce nitrogen (N) losses to the environment whilst maintaining or increasing farm profitability. This study examined the cost-effectiveness of stacking different combinations of five N leaching mitigation strategies within the whole farm system; 1) reduced N fertilizer input, 2) off-paddock infrastructure, 3) recycling N by growing maize silage on a dedicated area on the farm using effluent as a fertilizer source followed by a catch-crop, 4) dietary salt supplementation to dilute urinary N, and 5) applying a nitrification inhibitor (NI) to slow the release of nitrate in the soil. The reference point (baseline) was a typical current dairy farm (CF) system in the Waikato region of New Zealand. We modelled four Future Farm scenarios by stacking mitigation strategies as follows: baseline plus reduced N fertilizer input, reduced stocking rate, and off-paddock infrastructure (FF); FF plus a dedicated maize block (FFP); FFP plus dietary salt (FFPS); and FFPS plus NI (FFPSNI). These systems were modelled using the Whole Farm Model coupled with the Urine Patch Framework, and APSIM models, using observed climate and economic input data over five consecutive years from 2013-2018. Relative to CF, the FF system achieved a N leaching reduction of 31% with a reduction in profit of 16%. The FFP system had a smaller N leaching reduction (22%), but the reduction in profit was smaller (11%). The fully stacked system (FFPSNI) demonstrated the largest leaching reduction of 33%, but also the largest profit reduction of 27%, compared with the CF. Stacking these five N mitigation strategies can achieve substantial N leaching reductions at the farm-scale. Including a dedicated, effluent-fertilized maize block followed by a catch-crop as part of the stack can reduce the negative impact on profitability but has a trade-off in N leaching. Farmers will have to weigh up these compromises between profit and leaching, considering risk factors not modelled here.
Published: 2024
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26. Chi-square and normal inference in high-dimensional multi-task regression

Author: Bellec, Pierre C and Romon, Gabriel
Subjects: Mathematics - Statistics Theory, Statistics - Machine Learning
Abstract: The paper proposes chi-square and normal inference methodologies for the unknown coefficient matrix $B^*$ of size $p\times T$ in a Multi-Task (MT) linear model with $p$ covariates, $T$ tasks and $n$ observations under a row-sparse assumption on $B^*$. The row-sparsity $s$, dimension $p$ and number of tasks $T$ are allowed to grow with $n$. In the high-dimensional regime $p\ggg n$, in order to leverage row-sparsity, the MT Lasso is considered. We build upon the MT Lasso with a de-biasing scheme to correct for the bias induced by the penalty. This scheme requires the introduction of a new data-driven object, coined the interaction matrix, that captures effective correlations between noise vector and residuals on different tasks. This matrix is psd, of size $T\times T$ and can be computed efficiently. The interaction matrix lets us derive asymptotic normal and $\chi^2_T$ results under Gaussian design and $\frac{sT+s\log(p/s)}{n}\to0$ which corresponds to consistency in Frobenius norm. These asymptotic distribution results yield valid confidence intervals for single entries of $B^*$ and valid confidence ellipsoids for single rows of $B^*$, for both known and unknown design covariance $\Sigma$. While previous proposals in grouped-variables regression require row-sparsity $s\lesssim\sqrt n$ up to constants depending on $T$ and logarithmic factors in $n,p$, the de-biasing scheme using the interaction matrix provides confidence intervals and $\chi^2_T$ confidence ellipsoids under the conditions ${\min(T^2,\log^8p)}/{n}\to 0$ and $$ \frac{sT+s\log(p/s)+\|\Sigma^{-1}e_j\|_0\log p}{n}\to0, \quad \frac{\min(s,\|\Sigma^{-1}e_j\|_0)}{\sqrt n} \sqrt{[T+\log(p/s)]\log p}\to 0, $$ allowing row-sparsity $s\ggg\sqrt n$ when $\|\Sigma^{-1}e_j\|_0 \sqrt T\lll \sqrt{n}$ up to logarithmic factors.
Published: 2021

27. Derivatives and residual distribution of regularized M-estimators with application to adaptive tuning

Author: Bellec, Pierre C and Shen, Yiwei
Subjects: Mathematics - Statistics Theory, Statistics - Machine Learning
Abstract: This paper studies M-estimators with gradient-Lipschitz loss function regularized with convex penalty in linear models with Gaussian design matrix and arbitrary noise distribution. A practical example is the robust M-estimator constructed with the Huber loss and the Elastic-Net penalty and the noise distribution has heavy-tails. Our main contributions are three-fold. (i) We provide general formulae for the derivatives of regularized M-estimators $\hat\beta(y,X)$ where differentiation is taken with respect to both $y$ and $X$; this reveals a simple differentiability structure shared by all convex regularized M-estimators. (ii) Using these derivatives, we characterize the distribution of the residual $r_i = y_i-x_i^\top\hat\beta$ in the intermediate high-dimensional regime where dimension and sample size are of the same order. (iii) Motivated by the distribution of the residuals, we propose a novel adaptive criterion to select tuning parameters of regularized M-estimators. The criterion approximates the out-of-sample error up to an additive constant independent of the estimator, so that minimizing the criterion provides a proxy for minimizing the out-of-sample error. The proposed adaptive criterion does not require the knowledge of the noise distribution or of the covariance of the design. Simulated data confirms the theoretical findings, regarding both the distribution of the residuals and the success of the criterion as a proxy of the out-of-sample error. Finally our results reveal new relationships between the derivatives of $\hat\beta(y,X)$ and the effective degrees of freedom of the M-estimator, which are of independent interest.
Published: 2021

28. Asymptotic normality of robust $M$-estimators with convex penalty

Author: Bellec, Pierre C, Shen, Yiwei, and Zhang, Cun-Hui
Subjects: Mathematics - Statistics Theory, Statistics - Machine Learning
Abstract: This paper develops asymptotic normality results for individual coordinates of robust M-estimators with convex penalty in high-dimensions, where the dimension $p$ is at most of the same order as the sample size $n$, i.e, $p/n\le\gamma$ for some fixed constant $\gamma>0$. The asymptotic normality requires a bias correction and holds for most coordinates of the M-estimator for a large class of loss functions including the Huber loss and its smoothed versions regularized with a strongly convex penalty. The asymptotic variance that characterizes the width of the resulting confidence intervals is estimated with data-driven quantities. This estimate of the variance adapts automatically to low ($p/n\to0)$ or high ($p/n \le \gamma$) dimensions and does not involve the proximal operators seen in previous works on asymptotic normality of M-estimators. For the Huber loss, the estimated variance has a simple expression involving an effective degrees-of-freedom as well as an effective sample size. The case of the Huber loss with Elastic-Net penalty is studied in details and a simulation study confirms the theoretical findings. The asymptotic normality results follow from Stein formulae for high-dimensional random vectors on the sphere developed in the paper which are of independent interest.
Published: 2021

29. An atlas of healthy and injured cell states and niches in the human kidney

Author: Lake, Blue B., Menon, Rajasree, Winfree, Seth, Hu, Qiwen, Melo Ferreira, Ricardo, Kalhor, Kian, Barwinska, Daria, Otto, Edgar A., Ferkowicz, Michael, Diep, Dinh, Plongthongkum, Nongluk, Knoten, Amanda, Urata, Sarah, Mariani, Laura H., Naik, Abhijit S., Eddy, Sean, Zhang, Bo, Wu, Yan, Salamon, Diane, Williams, James C., Wang, Xin, Balderrama, Karol S., Hoover, Paul J., Murray, Evan, Marshall, Jamie L., Noel, Teia, Vijayan, Anitha, Hartman, Austin, Chen, Fei, Waikar, Sushrut S., Rosas, Sylvia E., Wilson, Francis P., Palevsky, Paul M., Kiryluk, Krzysztof, Sedor, John R., Toto, Robert D., Parikh, Chirag R., Kim, Eric H., Satija, Rahul, Greka, Anna, Macosko, Evan Z., Kharchenko, Peter V., Gaut, Joseph P., Hodgin, Jeffrey B., Eadon, Michael T., Dagher, Pierre C., El-Achkar, Tarek M., Zhang, Kun, Kretzler, Matthias, and Jain, Sanjay
Published: 2023
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30. The NHLBI Study on Long-terM OUtcomes after the Multisystem Inflammatory Syndrome In Children (MUSIC): Design and Objectives

Author: Truong, Dongngan T, Trachtenberg, Felicia L, Pearson, Gail D, Dionne, Audrey, Elias, Matthew D, Friedman, Kevin, Hayes, Kerri H, Mahony, Lynn, McCrindle, Brian W, Oster, Matthew E, Pemberton, Victoria, Powell, Andrew J, Russell, Mark W, Shekerdemian, Lara S, Son, Mary Beth, Taylor, Michael, Newburger, Jane W, Investigators, for the MUSIC Study, Giglia, Therese M, McHugh, Kimberly E, Atz, Andrew M, Pletzer, Scott A, Lang, Sean M, Payne, R Mark, Patel, Jyoti K, Pignatelli, Ricardo H, Sexson, Kristen, Lam, Christopher, Dragulescu, Andreea, Young, Rae SM, Gamulka, Beth, Krishnan, Anita, Anderson, Brett R, Farooqi, Kanwal M, Shakti, Divya, Parnell, Aimee S, Osakwe, Onyekachukwu J, Sykes, Michelle C, Morgan, Lerraughn, Owada, Carl Y, Forsha, Daniel, Carr, Michael R, Watanabe, Kae, Portman, Michael A, Dummer, Kristen B, Burns, Jane C, Tremoulet, Adriana H, Sharma, Kavita, Jone, Pei-Ni, Heizer, Michelle Hite Heather, Hasbani, Keren, Srivastava, Shubhika, Mitchell, Elizabeth C, Hebson, Camden L, Szmuszkovicz, Jacqueline R, Wong, Pierre C, Cheng, Andrew L, Votava-Smith, Jodie K, Wang, Shuo, Mohandas, Sindhu, Singh, Gautam K, Aggarwal, Sanjeev, Sanil, Yamuna, Bradford, Tamara T, Muniz, Juan Carlos G, Li, Jennifer S, Campbell, Michael Jay, Handler, Stephanie S, Shea, J Ryan, Hoffman, Timothy M, Franklin, Wayne J, Sabati, Arash A, Nowlen, Todd T, and Chrisant, Maryanne
Subjects: Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Heart Disease, Biomedical Imaging, Clinical Trials and Supportive Activities, Cardiovascular, Clinical Research, Prevention, Pediatric, Patient Safety, Adult, COVID-19, Child, Heart, Humans, Magnetic Resonance Imaging, National Heart, Lung, and Blood Institute (U.S.), SARS-CoV-2, Stroke Volume, Systemic Inflammatory Response Syndrome, United States, Ventricular Function, Left, Young Adult, MUSIC Study Investigators, Cardiorespiratory Medicine and Haematology, Public Health and Health Services, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract: BackgroundThe Long-terM OUtcomes after the Multisystem Inflammatory Syndrome In Children (MUSIC) study aims to characterize the frequency and time course of acute and long-term cardiac and non-cardiac sequelae in multisystem inflammatory syndrome in children associated with COVID-19 (MIS-C), which are currently poorly understood.MethodsThis multicenter observational cohort study will enroll at least 600 patients
Published: 2022

31. Convolutional Neural Network Denoising in Fluorescence Lifetime Imaging Microscopy (FLIM)

Author: Mannam, Varun, Zhang, Yide, Yuan, Xiaotong, Hato, Takashi, Dagher, Pierre C., Nichols, Evan L., Smith, Cody J., Dunn, Kenneth W., and Howard, Scott
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Systems and Control
Abstract: Fluorescence lifetime imaging microscopy (FLIM) systems are limited by their slow processing speed, low signal-to-noise ratio (SNR), and expensive and challenging hardware setups. In this work, we demonstrate applying a denoising convolutional network to improve FLIM SNR. The network will be integrated with an instant FLIM system with fast data acquisition based on analog signal processing, high SNR using high-efficiency pulse-modulation, and cost-effective implementation utilizing off-the-shelf radio-frequency components. Our instant FLIM system simultaneously provides the intensity, lifetime, and phasor plots \textit{in vivo} and \textit{ex vivo}. By integrating image denoising using the trained deep learning model on the FLIM data, provide accurate FLIM phasor measurements are obtained. The enhanced phasor is then passed through the K-means clustering segmentation method, an unbiased and unsupervised machine learning technique to separate different fluorophores accurately. Our experimental \textit{in vivo} mouse kidney results indicate that introducing the deep learning image denoising model before the segmentation effectively removes the noise in the phasor compared to existing methods and provides clearer segments. Hence, the proposed deep learning-based workflow provides fast and accurate automatic segmentation of fluorescence images using instant FLIM. The denoising operation is effective for the segmentation if the FLIM measurements are noisy. The clustering can effectively enhance the detection of biological structures of interest in biomedical imaging applications., Comment: SPIE Proceedings Volume 11648, Multiphoton Microscopy in the Biomedical Sciences XXI; 116481C (2021)
Published: 2021
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32. Participant Experience with Protocol Research Kidney Biopsies in the Kidney Precision Medicine Project

Author: Victoria-Castro, Angela M., Corona-Villalobos, Celia P., Xu, Alan Y., Onul, Ingrid, Huynh, Courtney, Chen, Sarah W., Ugwuowo, Ugochukwu, Sarkisova, Natalya, Dighe, Ashveena L., Blank, Kristina N., Blanc, Victoria M., Rose, Michael P., Himmelfarb, Jonathan, de Boer, Ian H., Tuttle, Katherine R., Roberts, Glenda V., Alexandrov, Theodore, Alloway, Rita R., Alpers, Charles E., Amodu, Afolarin A., Anderton, Christopher R., Anjani, Kavya, Appelbaum, Paul, Ardayfio, Joseph, Arora, Tanima, Ascani, Heather, El-Achkar, Tarek M., Aulisio, Mark, Azeloglu, Evren U., Balderes, Olivia, Balis, Ulysses G.J., Bansal, Shweta, Barasch, Jonathan M., Bansal, Shweta, Barkell, Alex, Barwinska, Daria, Basit, Mujeeb, Basta, Jeanine, Bebiak, Jack, Beck, Laurence H., Bender, Filitsa, Berglund, Ashley, Bernard, Lauren, Berrouet, Cecilia, Berry, Brooke, Bjornstad, Petter M., Blanc, Victoria M., Blank, Kristina N., Bledsoe, Sharon, Boada, Patrick, Bogen, Steve, Bomback, Andrew S., Bonevich, Nikole, Borner, Katy, Brown, Keith, Bueckle, Andreas, Burg, Ashley R., Burgess, Adam, Bush, Lakeshia, Bush, William S., Campbell, Catherine E., Campbell, Taneisha, Canetta, Pietro A., Cantley, Lloyd G., Caprioli, Richard M., Carson, Jonas, Chen, Sarah, Chen, Yijiang M., Cheng, Yinghua, Cimino, Jim, Colona, Mia R., Conser, Ninive C., Cooperman, Leslie, Crawford, Dana C., DʼAgati, Vivette D., Dagher, Pierre C., Daniel, Stephen, Daratha, Kenn, de Boer, Ian H., Diettman, Sabine M., Dighe, Ashveena L., Donohoe, Isabel, Dowd, Frederick, Dunn, Kenneth W., Eadon, Michael T., Eddy, Sean, Elder, Michele M., Ferkowicz, Michael J., Frey, Renee, Gadegbeku, Crystal A., Gaut, Joseph P., Gilliam, Matthew, Ginley, Brandon, Gisch, Debora, Goltsev, Yury, Gonzalez-Vicente, Agustin, Greka, Anna, Grewenow, Stephanie M., Hacohen, Nir, Hall, Daniel E., Hansen, Jens, Hayashi, Lynda, He, Cijang, He, Yougqun, Hedayati, S. Susan, Henderson, Joel M., Hendricks, Allen H., Herlitz, Leal, Herr, Bruce W., Himmelfarb, Jonathan, Hodgin, Jeffrey B., Hoofnagle, Andrew N., Hoover, Paul J., Ilori, Titlayo, Iyengar, Ravi, Jain, Sanjay, Jain, Yashvardhan, Janowczyk, Andrew, Jefferson, Nichole, Johansen, Camille, Jolly, Stacey, Kakade, Vijaykumar R., Kellum, John A., Kelly, Katherine J., Kermani, Asra, Kiryluk, Krzysztof, Knight, Richard, Koewler, Robert, Kretzler, Matthias, Kudose, Satoru, Lake, Blue B., Larson, Brandon, Laszik, Zoltan G., Lecker, Stewart H., Lee, Paul J., Lee, Simon C., Lienczewski, Chrysta, Limonte, Christine, Lu, Christopher Y., Lucarelli, Nicholas, Lukowski, Jessica, Luo, Jinghui, Lutnick, Brendon, Ma, Shihong, Madabhushi, Anant, Madhavan, Sethu M., Maikhor, Shana, Mariani, Laura H., Marshall, Jamie L., McClelland, Robyn L., McMahon, Gearoid M., Mehl, Karla, Ferreira, Ricardo Melo, Menez, Steven, Menon, Rajasree, Miller, R. Tyler, Moe, Orson W., Moledina, Dennis, Montellano, Richard, Mooney, Sean D., Morales, Martha Catalina, Mukatash, Tariq, Murugan, Raghavan, Nam, Yunbi, Nguyen, Jane, Nolan, Garry, Oʼtoole, John, Oliver, George (Holt), Onul, Ingrid, Otto, Edgar, Palevsky, Paul M., Palmer, Ellen, Pamreddy, Annapurna, Parikh, Chirag R., Parikh, Samir, Park, Christopher, Park, Harold, Pasa-Tolic, Ljiljana, Patel, Jiten, Patterson, Nathan, Phuong, Jim, Pillai, Anil, Pinkeney, Roy, Poggio, Emilio, Pollack, Ari, Prasad, Pottumarthi, Pyle, Laura, Quardokus, Ellen M., Randhawa, Parmjeet, Rauchman, Michael I., Record, Elizabeth, Rennke, Helmut, Rezaei, Kasra, Rike, Adele, Rivera, Marcelino, Roberts, Glenda V., Rosas, Sylvia E., Rosenberg, Avi, Rosengart, Matthew, Rovin, Brad, Roy, Neil, Sabatello, Maya, Sambandam, Kamalanathan, Sarder, Pinaki, Sarkisova, Natalya, Sarwal, Minnie, Saul, John, Schaub, Jennifer, Schmidt, Insa, Sealfon, Rachel, Sedor, John, Sendrey, Dianna, Shang, Ning, Shankland, Stuart, Shapiro, John P., Sharma, Kumar, Sharman, Kavya, Shaw, Melissa M., Shi, Tiffany, Shpigel, Anna, Sigdel, Tara, Slade, Austen, Snyder, Jamie, Spates-Harden, Kassandra, Spraggins, Jeffrey M., Srivastava, Anand, Steck, Becky, Stillman, Isaac, Stutzke, Christy, Su, Jing, Sun, Jennifer, Sutton, Timothy A., Taliercio, Jonathan, Tan, Roderick, Torrealba, Jose, Toto, Robert D., Troyanskaya, Olga, Tublin, Mitchell, Tuttle, Katherine R., Ugwuowo, Ugochukwu, Valerius, M. Todd, Van de Plas, Raf, Varela, German, Vazquez, Miguel, Velickovic, Dusan, Venkatachalam, Manjeri, Verma, Ashish, Victoria-Castro, Angela M., Vijayan, Anitha, Corona-Villalobos, Celia P., Vinovskis, Carissa, Viswanathan, Vidya S., Vita, Tina, Waikar, Sushrut, Wang, Ashley, Wang, Ruikang, Wang, Nancy, Weins, Astrid, Wen, Natasha, Wen, Yumeng, Wilcox, Adam, Williams, James C., Jr., Kayleen Williams, Williams, Mark, Wilson, Francis P., Winfree, Seth, Winters, James, Wofford, Stephanie, Wong, Aaron, Woodle, E. Steve, Xiong, Yuguang, Xu, Alan, Yadati, Pranav, Ye, Hongping, Yu, Guanghao, Zhang, Dianbo, Zhang, Guanshi, and Zhang, Kun
Published: 2024
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33. Uncertainty quantification for iterative algorithms in linear models with application to early stopping.

Author: Pierre C. Bellec and Kai Tan
Published: 2024
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34. miRNA and mRNA Signatures in Human Acute Kidney Injury Tissue

Author: Janosevic, Danielle, De Luca, Thomas, Ferreira, Ricardo Melo, Gisch, Debora L., Cheng, Ying-Hua, Hato, Takashi, Luo, Jinghui, Yang, Yingbao, Hodgin, Jeffrey B., Phillips, Carrie L., Dagher, Pierre C., and Eadon, Michael T.
Published: 2024
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35. Mechanistic Insights and Knowledge Gaps in the Effects of Radiation Therapy on Cardiac Arrhythmias

Author: Balaji, Poornima, Liulu, Xingzhou, Sivakumar, Sonaali, Chong, James J.H., Kizana, Eddy, Vandenberg, Jamie I., Hill, Adam P., Hau, Eric, and Qian, Pierre C.
Published: 2024
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36. Out-of-sample error estimate for robust M-estimators with convex penalty

Author: Bellec, Pierre C
Subjects: Mathematics - Statistics Theory, Statistics - Machine Learning
Abstract: A generic out-of-sample error estimate is proposed for robust $M$-estimators regularized with a convex penalty in high-dimensional linear regression where $(X,y)$ is observed and $p,n$ are of the same order. If $\psi$ is the derivative of the robust data-fitting loss $\rho$, the estimate depends on the observed data only through the quantities $\hat\psi = \psi(y-X\hat\beta)$, $X^\top \hat\psi$ and the derivatives $(\partial/\partial y) \hat\psi$ and $(\partial/\partial y) X\hat\beta$ for fixed $X$. The out-of-sample error estimate enjoys a relative error of order $n^{-1/2}$ in a linear model with Gaussian covariates and independent noise, either non-asymptotically when $p/n\le \gamma$ or asymptotically in the high-dimensional asymptotic regime $p/n\to\gamma'\in(0,\infty)$. General differentiable loss functions $\rho$ are allowed provided that $\psi=\rho'$ is 1-Lipschitz. The validity of the out-of-sample error estimate holds either under a strong convexity assumption, or for the $\ell_1$-penalized Huber M-estimator if the number of corrupted observations and sparsity of the true $\beta$ are bounded from above by $s_*n$ for some small enough constant $s_*\in(0,1)$ independent of $n,p$. For the square loss and in the absence of corruption in the response, the results additionally yield $n^{-1/2}$-consistent estimates of the noise variance and of the generalization error. This generalizes, to arbitrary convex penalty, estimates that were previously known for the Lasso., Comment: This version adds simulations for the nuclear norm penalty
Published: 2020

37. High spatial resolution seasonal crop yield forecasting for heterogeneous maize environments in Oromia, Ethiopia

Author: Kindie Tesfaye, Robel Takele, Vakhtang Shelia, Esayas Lemma, Addisu Dabale, Pierre C. Sibiry Traore, Dawit Solomon, and Gerrit Hoogenboom
Subjects: Crop modeling, Gridded simulations, East Africa, DSSAT, Forecast skill, Meteorology. Climatology, QC851-999, Social sciences (General), H1-99
Abstract: Seasonal climate variability determines crop productivity in Ethiopia, where rainfed smallholder farming systems dominate in the agriculture production. Under such conditions, a functional and granular spatial yield forecasting system could provide risk management options for farmers and agricultural and policy experts, leading to greater economic and social benefits under highly variable environmental conditions. Yet, there are currently only a few forecasting systems to support early decision making for smallholder agriculture in developing countries such as Ethiopia. To address this challenge, a study was conducted to evaluate a seasonal crop yield forecast methodology implemented in the CCAFS Regional Agricultural Forecasting Toolbox (CRAFT). CRAFT is a software platform that can run pre-installed crop models and use the Climate Predictability Tool (CPT) to produce probabilistic crop yield forecasts with various lead times. Here we present data inputs, model calibration, evaluation, and yield forecast results, as well as limitations and assumptions made during forecasting maize yield. Simulations were conducted on a 0.083° or ∼ 10 km resolution grid using spatially variable soil, weather, maize hybrids, and crop management data as inputs for the Cropping System Model (CSM) of the Decision Support System for Agrotechnology Transfer (DSSAT). CRAFT combines gridded crop simulations and a multivariate statistical model to integrate the seasonal climate forecast for the crop yield forecasting. A statistical model was trained using 29 years (1991–2019) data on the Nino-3.4 Sea surface temperature anomalies (SSTA) as gridded predictors field and simulated maize yields as the predictand. After model calibration the regional aggregated hindcast simulation from 2015 to 2019 performed well (RMSE = 164 kg/ha). The yield forecasts in both the absolute and relative to the normal yield values were conducted for the 2020 season using different predictor fields and lead times from a grid cell to the national level. Yield forecast uncertainties were presented in terms of cumulative probability distributions. With reliable data and rigorous calibration, the study successfully demonstrated CRAFT’s ability and applicability in forecasting maize yield for smallholder farming systems. Future studies should re-evaluate and address the importance of the size of agricultural areas while comparing aggregated simulated yields with yield data collected from a fraction of the target area.
Published: 2023
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38. De-biasing convex regularized estimators and interval estimation in linear models

Author: Bellec, Pierre C and Zhang, Cun-Hui
Subjects: Mathematics - Statistics Theory
Abstract: New upper bounds are developed for the $L_2$ distance between $\xi/\text{Var}[\xi]^{1/2}$ and linear and quadratic functions of $z\sim N(0,I_n)$ for random variables of the form $\xi=bz^\top f(z) - \text{div} f(z)$. The linear approximation yields a central limit theorem when the squared norm of $f(z)$ dominates the squared Frobenius norm of $\nabla f(z)$ in expectation. Applications of this normal approximation are given for the asymptotic normality of de-biased estimators in linear regression with correlated design and convex penalty in the regime $p/n \le \gamma$ for constant $\gamma\in(0,{\infty})$. For the estimation of linear functions $\langle a_0,\beta\rangle$ of the unknown coefficient vector $\beta$, this analysis leads to asymptotic normality of the de-biased estimate for most normalized directions $a_0$, where ``most'' is quantified in a precise sense. This asymptotic normality holds for any convex penalty if $\gamma<1$ and for any strongly convex penalty if $\gamma\ge 1$. In particular the penalty needs not be separable or permutation invariant. By allowing arbitrary regularizers, the results vastly broaden the scope of applicability of de-biasing methodologies to obtain confidence intervals in high-dimensions. In the absence of strong convexity for $p>n$, asymptotic normality of the de-biased estimate is obtained for the Lasso and the group Lasso under additional conditions. For general convex penalties, our analysis also provides prediction and estimation error bounds of independent interest., Comment: Manuscript title was updated; see former title at arXiv:912.11943v3
Published: 2019

39. High spatial resolution seasonal crop yield forecasting for heterogeneous maize environments in Oromia, Ethiopia

Author: Tesfaye, Kindie, Takele, Robel, Shelia, Vakhtang, Lemma, Esayas, Dabale, Addisu, Sibiry Traore, Pierre C., Solomon, Dawit, and Hoogenboom, Gerrit
Published: 2023
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40. Noise covariance estimation in multi-task high-dimensional linear models

Author: Tan, Kai, primary, Romon, Gabriel, additional, and Bellec, Pierre C., additional
Published: 2024
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41. Rationale and design of the Kidney Precision Medicine Project

Author: de Boer, Ian H, Alpers, Charles E, Azeloglu, Evren U, Balis, Ulysses GJ, Barasch, Jonathan M, Barisoni, Laura, Blank, Kristina N, Bomback, Andrew S, Brown, Keith, Dagher, Pierre C, Dighe, Ashveena L, Eadon, Michael T, El-Achkar, Tarek M, Gaut, Joseph P, Hacohen, Nir, He, Yongqun, Hodgin, Jeffrey B, Jain, Sanjay, Kellum, John A, Kiryluk, Krzysztof, Knight, Richard, Laszik, Zoltan G, Lienczewski, Chrysta, Mariani, Laura H, McClelland, Robyn L, Menez, Steven, Moledina, Dennis G, Mooney, Sean D, O’Toole, John F, Palevsky, Paul M, Parikh, Chirag R, Poggio, Emilio D, Rosas, Sylvia E, Rosengart, Matthew R, Sarwal, Minnie M, Schaub, Jennifer A, Sedor, John R, Sharma, Kumar, Steck, Becky, Toto, Robert D, Troyanskaya, Olga G, Tuttle, Katherine R, Vazquez, Miguel A, Waikar, Sushrut S, Williams, Kayleen, Wilson, Francis Perry, Zhang, Kun, Iyengar, Ravi, Kretzler, Matthias, Himmelfarb, Jonathan, Project, Kidney Precision Medicine, Lecker, Stewart, Stillman, Isaac, Waikar, Sushrut, Mcmahon, Gearoid, Weins, Astrid, Short, Samuel, Hoover, Paul, Aulisio, Mark, Cooperman, Leslie, Herlitz, Leal, O’Toole, John, Poggio, Emilio, Sedor, John, Jolly, Stacey, Appelbaum, Paul, Balderes, Olivia, Barasch, Jonathan, Bomback, Andrew, Canetta, Pietro A, d’Agati, Vivette D, Kudose, Satoru, Mehl, Karla, Radhakrishnan, Jai, Weng, Chenhua, Alexandrov, Theodore, Ashkar, Tarek, Barwinska, Daria, Dagher, Pierre, Dunn, Kenneth, Eadon, Michael, Ferkowicz, Michael, Kelly, Katherine, Sutton, Timothy, Winfree, Seth, Parikh, Chirag, Rosenberg, Avi, Villalobos, Pam, Malik, Rubab, Fine, Derek, Atta, Mohammed, Trujillo, Jose Manuel Monroy, Slack, Alison, Rosas, Sylvia, and Williams, Mark
Subjects: Clinical Research, Transplantation, Kidney Disease, Prevention, Renal and urogenital, Good Health and Well Being, Acute Kidney Injury, Adult, Humans, Kidney, Precision Medicine, Prospective Studies, Proteomics, Renal Insufficiency, Chronic, acute kidney injury, chronic kidney disease, diabetes, hypertension, precision medicine, Kidney Precision Medicine Project, Clinical Sciences, Urology & Nephrology
Abstract: Chronic kidney disease (CKD) and acute kidney injury (AKI) are common, heterogeneous, and morbid diseases. Mechanistic characterization of CKD and AKI in patients may facilitate a precision-medicine approach to prevention, diagnosis, and treatment. The Kidney Precision Medicine Project aims to ethically and safely obtain kidney biopsies from participants with CKD or AKI, create a reference kidney atlas, and characterize disease subgroups to stratify patients based on molecular features of disease, clinical characteristics, and associated outcomes. An additional aim is to identify critical cells, pathways, and targets for novel therapies and preventive strategies. This project is a multicenter prospective cohort study of adults with CKD or AKI who undergo a protocol kidney biopsy for research purposes. This investigation focuses on kidney diseases that are most prevalent and therefore substantially burden the public health, including CKD attributed to diabetes or hypertension and AKI attributed to ischemic and toxic injuries. Reference kidney tissues (for example, living-donor kidney biopsies) will also be evaluated. Traditional and digital pathology will be combined with transcriptomic, proteomic, and metabolomic analysis of the kidney tissue as well as deep clinical phenotyping for supervised and unsupervised subgroup analysis and systems biology analysis. Participants will be followed prospectively for 10 years to ascertain clinical outcomes. Cell types, locations, and functions will be characterized in health and disease in an open, searchable, online kidney tissue atlas. All data from the Kidney Precision Medicine Project will be made readily available for broad use by scientists, clinicians, and patients.
Published: 2021

42. Beyond Conventional Cardiac Resynchronisation Therapy: A Review of Electrophysiological Options in the Management of Chronic Heart Failure

Author: Malaty, Michael M., Sivagangabalan, Gopal, and Qian, Pierre C.
Published: 2023
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43. First order expansion of convex regularized estimators

Author: Bellec, Pierre C and Kuchibhotla, Arun K
Subjects: Mathematics - Statistics Theory, Statistics - Machine Learning
Abstract: We consider first order expansions of convex penalized estimators in high-dimensional regression problems with random designs. Our setting includes linear regression and logistic regression as special cases. For a given penalty function $h$ and the corresponding penalized estimator $\hat\beta$, we construct a quantity $\eta$, the first order expansion of $\hat\beta$, such that the distance between $\hat\beta$ and $\eta$ is an order of magnitude smaller than the estimation error $\|\hat{\beta} - \beta^*\|$. In this sense, the first order expansion $\eta$ can be thought of as a generalization of influence functions from the mathematical statistics literature to regularized estimators in high-dimensions. Such first order expansion implies that the risk of $\hat{\beta}$ is asymptotically the same as the risk of $\eta$ which leads to a precise characterization of the MSE of $\hat\beta$; this characterization takes a particularly simple form for isotropic design. Such first order expansion also leads to inference results based on $\hat{\beta}$. We provide sufficient conditions for the existence of such first order expansion for three regularizers: the Lasso in its constrained form, the lasso in its penalized form, and the Group-Lasso. The results apply to general loss functions under some conditions and those conditions are satisfied for the squared loss in linear regression and for the logistic loss in the logistic model., Comment: Accepted at NeurIPS 2019 and published at https://papers.nips.cc/paper/8606-first-order-expansion-of-convex-regularized-estimators . The version here includes the supplementary material
Published: 2019

44. The cost-free nature of optimally tuning Tikhonov regularizers and other ordered smoothers

Author: Bellec, Pierre C and Yang, Dana
Subjects: Mathematics - Statistics Theory, Statistics - Machine Learning
Abstract: We consider the problem of selecting the best estimator among a family of Tikhonov regularized estimators, or, alternatively, to select a linear combination of these regularizers that is as good as the best regularizer in the family. Our theory reveals that if the Tikhonov regularizers share the same penalty matrix with different tuning parameters, a convex procedure based on $Q$-aggregation achieves the mean square error of the best estimator, up to a small error term no larger than $C\sigma^2$, where $\sigma^2$ is the noise level and $C>0$ is an absolute constant. Remarkably, the error term does not depend on the penalty matrix or the number of estimators as long as they share the same penalty matrix, i.e., it applies to any grid of tuning parameters, no matter how large the cardinality of the grid is. This reveals the surprising "cost-free" nature of optimally tuning Tikhonov regularizers, in striking contrast with the existing literature on aggregation of estimators where one typically has to pay a cost of $\sigma^2\log(M)$ where $M$ is the number of estimators in the family. The result holds, more generally, for any family of ordered linear smoothers. This encompasses Ridge regression as well as Principal Component Regression. The result is extended to the problem of tuning Tikhonov regularizers with different penalty matrices.
Published: 2019

45. De-Biasing The Lasso With Degrees-of-Freedom Adjustment

Author: Bellec, Pierre C. and Zhang, Cun-Hui
Subjects: Mathematics - Statistics Theory, Statistics - Machine Learning
Abstract: This paper studies schemes to de-bias the Lasso in a linear model $y=X\beta+\epsilon$ where the goal is to construct confidence intervals for $a_0^T\beta$ in a direction $a_0$, where $X$ has iid $N(0,\Sigma)$ rows. We show that previously analyzed propositions to de-bias the Lasso require a modification in order to enjoy efficiency in a full range of sparsity. This modification takes the form of a degrees-of-freedom adjustment that accounts for the dimension of the model selected by Lasso. Let $s_0$ be the true sparsity. If $\Sigma$ is known and the ideal score vector proportional to $X\Sigma^{-1}a_0$ is used, the unadjusted de-biasing schemes proposed previously enjoy efficiency if $s_0\lll n^{2/3}$. However, if $s_0\ggg n^{2/3}$, the unadjusted schemes cannot be efficient in certain $a_0$: then it is necessary to modify existing procedures by a degrees-of-freedom adjustment. This modification grants asymptotic efficiency for any $a_0$ when $s_0/p\to 0$ and $s_0\log(p/s_0)/n \to 0$. If $\Sigma$ is unknown, efficiency is granted for general $a_0$ when $$\frac{s_0\log p}{n}+\min\Big\{\frac{s_\Omega\log p}{n},\frac{\|\Sigma^{-1}a_0\|_1\sqrt{\log p}}{\|\Sigma^{-1/2}a_0\|_2 \sqrt n}\Big\}+\frac{\min(s_\Omega,s_0)\log p}{\sqrt n}\to0$$ where $s_\Omega=\|\Sigma^{-1}a_0\|_0$, provided that the de-biased estimate is modified with the degrees-of-freedom adjustment. The dependence in $s_0,s_\Omega$ and $\|\Sigma^{-1}a_0\|_1$ is optimal. Our estimated score vector provides a novel methodology to handle dense $a_0$. Our analysis shows that the degrees-of-freedom adjustment is not needed when the initial bias in direction $a_0$ is small, which is granted under stringent conditions on $\Sigma^{-1}$. The main proof argument is an interpolation path similar to that typically used to derive Slepian's lemma. It yields a new $\ell_\infty$ error bound for the Lasso which is of independent interest.
Published: 2019

46. Concentration of quadratic forms under a Bernstein moment assumption

Author: Bellec, Pierre C
Subjects: Mathematics - Statistics Theory
Abstract: A concentration result for quadratic form of independent subgaussian random variables is derived. If the moments of the random variables satisfy a "Bernstein condition", then the variance term of the Hanson-Wright inequality can be improved. The Bernstein condition is satisfied, for instance, by all log-concave subgaussian distributions., Comment: This short note presents a result that initially appeared in arXiv:1410.0346v1 (see Assumption 3.3). The result was later removed from arXiv:1410.0346 and the published version https://projecteuclid.org/euclid.aos/1519268423 due to space constraints
Published: 2019

47. Integrated Cytometry With Machine Learning Applied to High-Content Imaging of Human Kidney Tissue for In Situ Cell Classification and Neighborhood Analysis

Author: Winfree, Seth, McNutt, Andrew T., Khochare, Suraj, Borgard, Tyler J., Barwinska, Daria, Sabo, Angela R., Ferkowicz, Michael J., Williams, James C., Jr., Lingeman, James E., Gulbronson, Connor J., Kelly, Katherine J., Sutton, Timothy A., Dagher, Pierre C., Eadon, Michael T., Dunn, Kenneth W., and El-Achkar, Tarek M.
Published: 2023
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48. Second order Stein: SURE for SURE and other applications in high-dimensional inference

Author: Bellec, Pierre C and Zhang, Cun-Hui
Subjects: Mathematics - Statistics Theory
Abstract: Stein's formula states that a random variable of the form $z^\top f(z) - \text{div} f(z)$ is mean-zero for functions $f$ with integrable gradient. Here, $\text{div} f$ is the divergence of the function $f$ and $z$ is a standard normal vector. This paper aims to propose a Second Order Stein formula to characterize the variance of such random variables for all functions $f(z)$ with square integrable gradient, and to demonstrate the usefulness of this formula in various applications. In the Gaussian sequence model, a consequence of Stein's formula is Stein's Unbiased Risk Estimate (SURE), an unbiased estimate of the mean squared risk for almost any estimator $\hat\mu$ of the unknown mean. A first application of the Second Order Stein formula is an Unbiased Risk Estimate for SURE itself (SURE for SURE): an unbiased estimate {providing} information about the squared distance between SURE and the squared estimation error of $\hat\mu$. SURE for SURE has a simple form as a function of the data and is applicable to all $\hat\mu$ with square integrable gradient, e.g. the Lasso and the Elastic Net. In addition to SURE for SURE, the following applications are developed: (1) Upper bounds on the risk of SURE when the estimation target is the mean squared error; (2) Confidence regions based on SURE; (3) Oracle inequalities satisfied by SURE-tuned estimates; (4) An upper bound on the variance of the size of the model selected by the Lasso; (5) Explicit expressions of SURE for SURE for the Lasso and the Elastic-Net; (6) In the linear model, a general semi-parametric scheme to de-bias a differentiable initial estimator for inference of a low-dimensional projection of the unknown $\beta$, with a characterization of the variance after de-biasing; and (7) An accuracy analysis of a Gaussian Monte Carlo scheme to approximate the divergence of functions $f: R^n\to R^n$.
Published: 2018

49. Shone’s Complex

Author: Wang, Shuo, primary and Wong, Pierre C., additional
Published: 2023
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50. Trends in mortality in people with HIV from 1999 to 2020: a multi-cohort collaboration

Author: Tusch, E, Ryom, L, Pelchen-Matthews, A, Mocroft, A, Elbirt, D, Oprea, C, Günthard, H, Staehelin, C, Zangerle, R, Suarez, I, Vehreschild, J, Wit, F, Menozzi, M, d'Arminio Monforte, A, Spagnuolo, V, Pradier, C, Carlander, C, Suanzes, P, Wasmuth, J, Carr, A, Petoumenos, K, Borgans, F, Bonnet, F, De Wit, S, El-Sadr, W, Neesgaard, B, Jaschinski, N, Greenberg, L, Hosein, S, Gallant, J, Vannappagari, V, Young, L, Sabin, C, Lundgren, J, Peters, L, Reekie, J, Calvo, G, Dabis, F, Kirk, O, Law, M, Monforte, A, Morfeldt, L, Reiss, P, Weber, R, Lind-Thomsen, A, Brandt, R, Hillebreght, M, Zaheri, S, Scherrer, A, Schöni-Affolter, F, Rickenbach, M, Tavelli, A, Fanti, I, Leleux, O, Mourali, J, Marec, F, Boerg, E, Thulin, E, Sundström, A, Bartsch, G, Thompsen, G, Necsoi, C, Delforge, M, Fontas, E, Caissotti, C, Dollet, K, Mateu, S, Torres, F, Blance, A, Huang, R, Puhr, R, Laut, K, Kristensen, D, Phillips, A, Kamara, D, Smith, C, Hatleberg, C, Raben, D, Matthews, C, Bojesen, A, Grevsen, A, Powderly, B, Shortman, N, Moecklinghoff, C, Reilly, G, Franquet, X, Smit, C, Ross, M, Fux, C, Morlat, P, Friis-Møller, N, Kowalska, J, Bohlius, J, Bower, M, Fätkenheuer, G, Grulich, A, Sjøl, A, Meidahl, P, Iversen, J, Reiss, C, Hillebregt, M, Prins, J, Kuijpers, T, Scherpbier, H, van der Meer, J, Godfried, M, van der Poll, T, Nellen, F, Geerlings, S, van Vugt, M, Pajkrt, D, Bos, J, Wiersinga, W, van der Valk, M, Goorhuis, A, Hovius, J, van Eden, J, Henderiks, A, van Hes, A, Mutschelknauss, M, Nobel, H, Pijnappel, F, Jurriaans, S, Back, N, Zaaijer, H, Berkhout, B, Cornelissen, M, Schinkel, C, Thomas, X, Ziekenhuis, A, van den Berge, M, Stegeman, A, Baas, S, de Looff, L, Versteeg, D, Ziekenhuis, C, Pronk, M, Ammerlaan, H, De Munnik, E, Jansz, A, Tjhie, J, Wegdam, M, Deiman, B, Scharnhorst, V, van der Plas, A, Weijsenfeld, A, van der Ende, M, De Vries-Sluijs, T, van Gorp, E, Schurink, C, Nouwen, J, Verbon, A, Rijnders, B, Bax, H, van der Feltz, M, Bassant, N, van Beek, J, Vriesde, M, van Zonneveld, L, de Oude-Lubbers, A, van den Berg-Cameron, H, Bruinsma-Broekman, F, de Groot, J, de Man, M, Boucher, C, Koopmans, M, van Kampen, J, Pas, S, Mc–sophia, E, Driessen, G, van Rossum, A, van der Knaap, L, Visser, E, Branger, J, Rijkeboer-Mes, A, de Ven, C, Ziekenhuis, H, Schippers, E, van Nieuwkoop, C, van IJperen, J, Geilings, J, van der Hut, G, Franck, P, van Eeden, A, Brokking, W, Groot, M, Elsenburg, L, Damen, M, Kwa, I, Groeneveld, P, Bouwhuis, J, van den Berg, J, van Hulzen, A, van der Bliek, G, Bor, P, Bloembergen, P, Wolfhagen, M, Ruijs, G, Kroon, F, de Boer, M, Bauer, M, Jolink, H, Vollaard, A, Dorama, W, van Holten, N, Claas, E, Wessels, E, den Hollander, J, Pogany, K, Roukens, A, Kastelijns, M, Smit, J, Smit, E, Struik-Kalkman, D, Tearno, C, Bezemer, M, van Niekerk, T, Pontesilli, O, Lowe, S, Lashof, A, Posthouwer, D, Ackens, R, Schippers, J, Vergoossen, R, Weijenberg-Maes, B, van Loo, I, Havenith, T, Leyten, E, Gelinck, L, van Hartingsveld, A, Meerkerk, C, Wildenbeest, G, Mutsaers, J, Jansen, C, Mulder, J, Vrouenraets, S, Lauw, F, van Broekhuizen, M, Paap, H, Vlasblom, D, Smits, P, Zuiderzee, M, Weijer, S, El Moussaoui, R, Bosma, A, van Vonderen, M, van Houte, D, Kampschreur, L, Dijkstra, K, Faber, S, Weel, J, Kootstra, G, Delsing, C, van der Burg-van de Plas, M, Heins, H, Lucas, E, Kortmann, W, van Twillert, G, Stuart, J, Diederen, B, Pronk, D, van Truijen-Oud, F, van der Reijden, W, Jansen, R, Brinkman, K, van den Berk, G, Blok, W, Frissen, P, Lettinga, K, Schouten, W, Veenstra, J, Brouwer, C, Geerders, G, Hoeksema, K, Kleene, M, van der Meché, I, Spelbrink, M, Sulman, H, Toonen, A, Wijnands, S, Kwa, D, Witte, E, Koopmans, P, Keuter, M, van der Ven, A, ter Hofstede, H, Dofferhoff, A, van Crevel, R, Albers, M, Bosch, M, Grintjes-Huisman, K, Zomer, B, Stelma, F, Rahamat-Langendoen, J, Burger, D, Richter, C, Gisolf, E, Hassing, R, ter Beest, G, van Bentum, P, Langebeek, N, Tiemessen, R, Swanink, C, van Lelyveld, S, Soetekouw, R, Hulshoff, N, van der Prijt, L, van der Swaluw, J, Bermon, N, Herpers, B, Veenendaal, D, Verhagen, D, van Wijk, M, Ziekenhuis, S, van Kasteren, M, Brouwer, A, de Wiel, B, Kuipers, M, Santegoets, R, van der Ven, B, Marcelis, J, Buiting, A, Kabel, P, Bierman, W, Scholvinck, H, Wilting, K, Stienstra, Y, Jonge, H, van der Meulen, P, de Weerd, D, Ludwig-Roukema, J, Niesters, H, Riezebos-Brilman, A, van Leer-Buter, C, Knoester, M, Hoepelman, A, Mudrikova, T, Ellerbroek, P, Oosterheert, J, Arends, J, Barth, R, Wassenberg, M, Schadd, E, van Elst-Laurijssen, D, van Oers-Hazelzet, E, Vervoort, S, van Berkel, M, Schuurman, R, Verduyn-Lunel, F, Wensing, A, Peters, E, van Agtmael, M, Bomers, M, de Vocht, J, Heitmuller, M, Laan, L, Pettersson, A, Vandenbroucke-Grauls, C, Ang, C, Kinderziekenhuis, W, Geelen, S, Wolfs, T, Bont, L, Nauta, N, Bezemer, D, van Sighem, A, Boender, T, de Jong, A, Bergsma, D, Hoekstra, P, de Lang, A, Grivell, S, Jansen, A, Rademaker, M, Raethke, M, Meijering, R, Schnörr, S, de Groot, L, van den Akker, M, Bakker, Y, Claessen, E, El Berkaoui, A, Koops, J, Kruijne, E, Lodewijk, C, Munjishvili, L, Peeck, B, Ree, C, Regtop, R, Ruijs, Y, Rutkens, T, van de Sande, L, Schoorl, M, Timmerman, A, Tuijn, E, Veenenberg, L, van der Vliet, S, Wisse, A, Woudstra, T, Tuk, B, Dupon, M, Gaborieau, V, Lacoste, D, Malvy, D, Mercié, P, Neau, D, Pellegrin, J, Tchamgoué, S, Lazaro, E, Cazanave, C, Vandenhende, M, Vareil, M, Gérard, Y, Blanco, P, Bouchet, S, Breilh, D, Fleury, H, Pellegrin, I, Chêne, G, Thiébaut, R, Wittkop, L, Lawson-Ayayi, S, Gimbert, A, Desjardin, S, Lacaze-Buzy, L, Petrov-Sanchez, V, André, K, Bernard, N, Caubet, O, Caunegre, L, Chossat, I, Courtault, C, Dauchy, F, De Witte, S, Dondia, D, Duffau, P, Dutronc, H, Farbos, S, Faure, I, Ferrand, H, Gerard, Y, Greib, C, Hessamfar, M, Imbert, Y, Lataste, P, Marie, J, Mechain, M, Monlun, E, Ochoa, A, Pistone, T, Raymond, I, Receveur, M, Rispal, P, Sorin, L, Valette, C, Viallard, J, Wille, H, Wirth, G, Lafon, M, Trimoulet, P, Bellecave, P, Tumiotto, C, Haramburu, F, Miremeont-Salamé, G, Blaizeau, M, Decoin, M, Hannapier, C, Lenaud, E, Pougetoux, A, Delveaux, S, D’Ivernois, C, Diarra, F, Uwamaliya-Nziyumvira, B, Palmer, G, Conte, V, Sapparrart, V, Law, C, Moore, R, Edwards, S, Hoy, J, Watson, K, Roth, N, Lau, H, Bloch, M, Baker, D, Cooper, D, O’Sullivan, M, Nolan, D, Guelfi, G, Calvo, C, Domingo, P, Sambeat, M, Gatell, J, Del Cacho, E, Cadafalch, J, Fuster, M, Codina, C, Sirera, G, Vaqué, A, Clumeck, N, Gennotte, A, Gerard, M, Kabeya, K, Konopnicki, D, Libois, A, Martin, C, Payen, M, Semaille, P, Van Laethem, Y, Neaton, C, Krum, E, Thompson, G, Wentworth, D, Luskin-Hawk, R, Telzak, E, Abrams, D, Cohn, D, Markowitz, N, Arduino, R, Mushatt, D, Friedland, G, Perez, G, Tedaldi, E, Fisher, E, Gordin, F, Crane, L, Sampson, J, Baxter, J, Gazzard, B, Horban, A, Karpov, I, Losso, M, Pedersen, C, Ristola, M, Rockstroh, J, Fischer, A, Larsen, J, Podlekareva, D, Cozzi-Lepri, A, Shepherd, L, Schultze, A, Amele, S, Kundro, M, Schmied, B, Wien, P, Vassilenko, A, Mitsura, V, Paduto, D, Florence, E, Vandekerckhove, L, Hadziosmanovic, V, Begovac, J, Machala, L, Jilich, D, Sedlacek, D, Kronborg, G, Benfield, T, Gerstoft, J, Katzenstein, T, Møller, N, Ostergaard, L, Wiese, L, Nielsen, L, Zilmer, K, Smidt, J, Siseklinik, N, Aho, I, Viard, J, Duvivier, C, Schmidt, R, Degen, O, Stellbrink, H, Stefan, C, Goethe, J, Bogner, J, Chkhartishvili, N, Gargalianos, P, Xylomenos, G, Armenis, K, Sambatakou, H, Szlávik, J, Gottfredsson, M, Mulcahy, F, Yust, I, Turner, D, Burke, M, Shahar, E, Hassoun, G, Elinav, H, Haouzi, M, Sthoeger, Z, Esposito, R, Mazeu, I, Mussini, C, Mazzotta, F, Gabbuti, A, Annunziata, O, Vullo, V, Lichtner, M, Zaccarelli, M, Antinori, A, Acinapura, R, Plazzi, M, Lazzarin, A, Castagna, A, Gianotti, N, Galli, M, Ridolfo, A, Rozentale, B, Uzdaviniene, V, Matulionyte, R, Staub, T, Hemmer, R, Ormaasen, V, Maeland, A, Bruun, J, Knysz, B, Gasiorowski, J, Inglot, M, Bakowska, E, Flisiak, R, Grzeszczuk, A, Parczewski, M, Maciejewska, K, Aksak-Was, B, Beniowski, M, Mularska, E, Smiatacz, T, Gensing, M, Jablonowska, E, Malolepsza, E, Wojcik, K, Mozer-Lisewska, I, Caldeira, L, Mansinho, K, Maltez, F, Radoi, R, Panteleev, A, Panteleev, O, Yakovlev, A, Trofimora, T, Khromova, I, Kuzovatova, E, Blokhina, I, Novogrod, N, Borodulina, E, Vdoushkina, E, Jevtovic, D, Tomazic, J, Miró, J, Moreno, S, Rodriguez, J, Clotet, B, Jou, A, Paredes, R, Tural, C, Puig, J, Bravo, I, Gutierrez, M, Mateo, G, Laporte, J, Sonnerborg, A, Brännström, I, Flamholc, L, Cavassini, M, Calmy, A, Furrer, H, Battegay, M, Schmid, P, Kuznetsova, A, Kyselyova, G, Sluzhynska, M, Johnson, A, Simons, E, Johnson, M, Orkin, C, Weber, J, Scullard, G, Clarke, A, Leen, C, Morfeldt, C, Thulin, G, Åkerlund, B, Koppel, K, Karlsson, A, Håkangård, C, Castelli, F, Cauda, R, Perri, G, Iardino, R, Ippolito, G, Marchetti, G, Perno, C, von Schloesser, F, Viale, P, Ceccherini-Silberstein, F, Girardi, E, Caputo, S, Puoti, M, Andreoni, M, Ammassari, A, Balotta, C, Bandera, A, Bonfanti, P, Bonora, S, Borderi, M, Calcagno, A, Calza, L, Capobianchi, M, Cingolani, A, Cinque, P, De Luca, A, Biagio, A, Gori, A, Guaraldi, G, Lapadula, G, Madeddu, G, Maggiolo, F, Marcotullio, S, Monno, L, Nozza, S, Roldan, E, Rossotti, R, Rusconi, S, Santoro, M, Saracino, A, Galli, L, Lorenzini, P, Rodano, A, Shanyinde, M, Carletti, F, Carrara, S, Caro, A, Graziano, S, Petrone, F, Prota, G, Quartu, S, Truffa, S, Giacometti, A, Costantini, A, Barocci, V, Angarano, G, Santoro, C, Suardi, C, Donati, V, Verucchi, G, Minardi, C, Quirino, T, Abeli, C, Manconi, P, Piano, P, Cacopardo, B, Celesia, B, Vecchiet, J, Falasca, K, Pan, A, Lorenzotti, S, Sighinolfi, L, Segala, D, Vichi, F, Cassola, G, Viscoli, C, Alessandrini, A, Bobbio, N, Mazzarello, G, Mastroianni, C, Belvisi, V, Caramma, I, Chiodera, A, Milini, P, Rizzardini, G, Moioli, M, Piolini, R, Salpietro, S, Tincati, C, Puzzolante, C, Abrescia, N, Chirianni, A, Borgia, G, Orlando, R, 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Stellbrink, H J, Goethe, J W, Sthoeger, Z M, Monforte, A D’Arminio, Annunziata, Ospedale S Maria, Blokhina, I N, Novogrod, Nizhny, Gatell, J M, Miró, J M, Rodriguez, J M, Laporte, J M, Johnson, A M, Johnson, M A, Morfeldt, Central L, Perri, G Di, Marchetti, G C, Perno, C F, Caputo, S Lo, Capobianchi, M R, Biagio, A Di, Roldan, E Quiros, Santoro, M M, Caro, A Di, Manconi, P E, Moioli, M C, Ridolfo, A L, Martino, F Di, Cattelan, A M, Ursitti, M A, Sulekova, L Fontanelli, Plazzi, M M, Del Vecchio, R Fontana, Giuli, C Di, Orofino, G C, Roger, P M, Braun, D L, Bucher, H C, Günthard, H F, Hirsch, H H, Kouyos, R D, de Tejada, B Martinez, Metzner, K J, Scherrer, A U, Valk, Marc vd, Han, W Min, Saint-Pierre, C H U, Miro, J M, Wasmuth, J C, Vehreschild, J J, McNicholl, I, Williams, E D, Volny-Anne, R Campo Alain, Dedes, Nikos, Mendão, Luis, Jakobsen, M L, Kumar, L Ramesh, Elsing, T W, and null, null
Abstract: Background: Mortality among people with HIV declined with the introduction of combination antiretroviral therapy. We investigated trends over time in all-cause and cause-specific mortality in people with HIV from 1999-2020. Methods: Data were collected from the D:A:D cohort from 1999 through January 2015 and RESPOND from October 2017 through 2020. Age-standardized all-cause and cause-specific mortality rates, classified using Coding Causes of Death in HIV (CoDe), were calculated. Poisson regression models were used to assess mortality trends over time. Results: Among 55716 participants followed for a median of 6 years (IQR 3-11), 5263 participants died (crude mortality rate [MR] 13.7/1000 PYFU; 95%CI 13.4-14.1). Changing patterns of mortality were observed with AIDS as the most common cause of death between 1999- 2009 (n = 952, MR 4.2/1000 PYFU; 95%CI 4.0-4.5) and non-AIDS defining malignancy (NADM) from 2010 -2020 (n = 444, MR 2.8/1000 PYFU; 95%CI 2.5-3.1). In multivariable analysis, all-cause mortality declined over time (adjusted mortality rate ratio [aMRR] 0.97 per year; 95%CI 0.96, 0.98), mostly from 1999 through 2010 (aMRR 0.96 per year; 95%CI 0.95-0.97), and with no decline shown from 2011 through 2020 (aMRR 1·00 per year; 95%CI 0·96-1·05). Mortality due all known causes except NADM also declined over the entire follow-up period. Conclusion: Mortality among people with HIV in the D:A:D and/or RESPOND cohorts decreased between 1999 and 2009 and was stable over the period from 2010 through 2020. The decline in mortality rates was not fully explained by improvements in immunologic-virologic status or other risk factors.
Published: 2024

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