Keeping him in office prevents the left from destroying America with their socialistic ideology, a letter to the editor says.

Until we have a vaccine, the road to opening is through a health care system which can handle the infection, a letter to the editor says.

Until we have a vaccine, the road to opening is through a health care system which can handle the infection, a letter to the editor says.

Adriaan Sticker
Apr 22, 2020; 0:RA119.001624v1-mcp.RA119.001624
Research

The presentation of peptides on class I human leukocyte antigen (HLA-I) molecules plays a central role in immune recognition of infected or malignant cells. In cancer, non-self HLA-I ligands can arise from many different alterations, including non-synonymous mutations, gene fusion, cancer-specific alternative mRNA splicing or aberrant post-translational modifications. Identifying HLA-I ligands remains a challenging task that requires either heavy experimental work for in vivo identification or optimized bioinformatics tools for accurate predictions. To date, no HLA-I ligand predictor includes post-translational modifications. To fill this gap, we curated phosphorylated HLA-I ligands from several immunopeptidomics studies (including six newly measured samples) covering 72 HLA-I alleles and retrieved a total of 2,066 unique phosphorylated peptides. We then expanded our motif deconvolution tool to identify precise binding motifs of phosphorylated HLA-I ligands. Our results reveal a clear enrichment of phosphorylated peptides among HLA-C ligands and demonstrate a prevalent role of both HLA-I motifs and kinase motifs on the presentation of phosphorylated peptides. These data further enabled us to develop and validate the first predictor of interactions between HLA-I molecules and phosphorylated peptides.

Label-Free Quantitative mass spectrometry based workflows for differential expression (DE) analysis of proteins impose important challenges on the data analysis due to peptide-specific effects and context dependent missingness of peptide intensities. Peptide-based workflows, like MSqRob, test for DE directly from peptide intensities and outperform summarization methods which first aggregate MS1 peptide intensities to protein intensities before DE analysis. However, these methods are computationally expensive, often hard to understand for the non-specialised end-user, and do not provide protein summaries, which are important for visualisation or downstream processing. In this work, we therefore evaluate state-of-the-art summarization strategies using a benchmark spike-in dataset and discuss why and when these fail compared to the state-of-the-art peptide based model, MSqRob. Based on this evaluation, we propose a novel summarization strategy, MSqRobSum, which estimates MSqRob’s model parameters in a two-stage procedure circumventing the drawbacks of peptide-based workflows. MSqRobSum maintains MSqRob’s superior performance, while providing useful protein expression summaries for plotting and downstream analysis. Summarising peptide to protein intensities considerably reduces the computational complexity, the memory footprint and the model complexity, and makes it easier to disseminate DE inferred on protein summaries. Moreover, MSqRobSum provides a highly modular analysis framework, which provides researchers with full flexibility to develop data analysis workflows tailored towards their specific applications.

HE Hohmeier
Mar 1, 2000; 49:424-430
Articles

We consider the problem of clustering with the longest-leg path distance (LLPD) metric, which is informative for elongated and irregularly shaped clusters. We prove finite-sample guarantees on the performance of clustering with respect to this metric when random samples are drawn from multiple intrinsically low-dimensional clusters in high-dimensional space, in the presence of a large number of high-dimensional outliers. By combining these results with spectral clustering with respect to LLPD, we provide conditions under which the Laplacian eigengap statistic correctly determines the number of clusters for a large class of data sets, and prove guarantees on the labeling accuracy of the proposed algorithm. Our methods are quite general and provide performance guarantees for spectral clustering with any ultrametric. We also introduce an efficient, easy to implement approximation algorithm for the LLPD based on a multiscale analysis of adjacency graphs, which allows for the runtime of LLPD spectral clustering to be quasilinear in the number of data points.

Consider an unknown smooth function $f: [0,1]^d ightarrow mathbb{R}$, and assume we are given $n$ noisy mod 1 samples of $f$, i.e., $y_i = (f(x_i) + eta_i) mod 1$, for $x_i in [0,1]^d$, where $eta_i$ denotes the noise. Given the samples $(x_i,y_i)_{i=1}^{n}$, our goal is to recover smooth, robust estimates of the clean samples $f(x_i) mod 1$. We formulate a natural approach for solving this problem, which works with angular embeddings of the noisy mod 1 samples over the unit circle, inspired by the angular synchronization framework. This amounts to solving a smoothness regularized least-squares problem -- a quadratically constrained quadratic program (QCQP) -- where the variables are constrained to lie on the unit circle. Our proposed approach is based on solving its relaxation, which is a trust-region sub-problem and hence solvable efficiently. We provide theoretical guarantees demonstrating its robustness to noise for adversarial, as well as random Gaussian and Bernoulli noise models. To the best of our knowledge, these are the first such theoretical results for this problem. We demonstrate the robustness and efficiency of our proposed approach via extensive numerical simulations on synthetic data, along with a simple least-squares based solution for the unwrapping stage, that recovers the original samples of $f$ (up to a global shift). It is shown to perform well at high levels of noise, when taking as input the denoised modulo $1$ samples. Finally, we also consider two other approaches for denoising the modulo 1 samples that leverage tools from Riemannian optimization on manifolds, including a Burer-Monteiro approach for a semidefinite programming relaxation of our formulation. For the two-dimensional version of the problem, which has applications in synthetic aperture radar interferometry (InSAR), we are able to solve instances of real-world data with a million sample points in under 10 seconds, on a personal laptop.

We consider the standard model of distributed optimization of a sum of functions $F(mathbf z) = sum_{i=1}^n f_i(mathbf z)$, where node $i$ in a network holds the function $f_i(mathbf z)$. We allow for a harsh network model characterized by asynchronous updates, message delays, unpredictable message losses, and directed communication among nodes. In this setting, we analyze a modification of the Gradient-Push method for distributed optimization, assuming that (i) node $i$ is capable of generating gradients of its function $f_i(mathbf z)$ corrupted by zero-mean bounded-support additive noise at each step, (ii) $F(mathbf z)$ is strongly convex, and (iii) each $f_i(mathbf z)$ has Lipschitz gradients. We show that our proposed method asymptotically performs as well as the best bounds on centralized gradient descent that takes steps in the direction of the sum of the noisy gradients of all the functions $f_1(mathbf z), ldots, f_n(mathbf z)$ at each step.

This work is concerned with the non-negative rank-1 robust principal component analysis (RPCA), where the goal is to recover the dominant non-negative principal components of a data matrix precisely, where a number of measurements could be grossly corrupted with sparse and arbitrary large noise. Most of the known techniques for solving the RPCA rely on convex relaxation methods by lifting the problem to a higher dimension, which significantly increase the number of variables. As an alternative, the well-known Burer-Monteiro approach can be used to cast the RPCA as a non-convex and non-smooth $ell_1$ optimization problem with a significantly smaller number of variables. In this work, we show that the low-dimensional formulation of the symmetric and asymmetric positive rank-1 RPCA based on the Burer-Monteiro approach has benign landscape, i.e., 1) it does not have any spurious local solution, 2) has a unique global solution, and 3) its unique global solution coincides with the true components. An implication of this result is that simple local search algorithms are guaranteed to achieve a zero global optimality gap when directly applied to the low-dimensional formulation. Furthermore, we provide strong deterministic and probabilistic guarantees for the exact recovery of the true principal components. In particular, it is shown that a constant fraction of the measurements could be grossly corrupted and yet they would not create any spurious local solution.

Saralees Nadarajah, Yuancheng Si.

Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 1, 183--187.

Abstract:
Da Paz, Balakrishnan and Bazan [Braz. J. Probab. Stat. 33 (2019), 455–479] introduced the L-logistic distribution, studied its properties including estimation issues and illustrated a data application. This note derives a closed form expression for moment properties of the distribution. Some computational issues are discussed.

Flávio B. Gonçalves, Marcos O. Prates, Victor Hugo Lachos.

Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 1, 51--70.

Abstract:
In this paper, we present a novel methodology to perform Bayesian model selection in linear models with heavy-tailed distributions. We consider a finite mixture of distributions to model a latent variable where each component of the mixture corresponds to one possible model within the symmetrical class of normal independent distributions. Naturally, the Gaussian model is one of the possibilities. This allows for a simultaneous analysis based on the posterior probability of each model. Inference is performed via Markov chain Monte Carlo—a Gibbs sampler with Metropolis–Hastings steps for a class of parameters. Simulated examples highlight the advantages of this approach compared to a segregated analysis based on arbitrarily chosen model selection criteria. Examples with real data are presented and an extension to censored linear regression is introduced and discussed.

Rosineide F. da Paz, Narayanaswamy Balakrishnan, Jorge Luis Bazán.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 3, 455--479.

Abstract:
Tadikamalla and Johnson [ Biometrika 69 (1982) 461–465] developed the $L_{B}$ distribution to variables with bounded support by considering a transformation of the standard Logistic distribution. In this manuscript, a convenient parametrization of this distribution is proposed in order to develop regression models. This distribution, referred to here as L-Logistic distribution, provides great flexibility and includes the uniform distribution as a particular case. Several properties of this distribution are studied, and a Bayesian approach is adopted for the parameter estimation. Simulation studies, considering prior sensitivity analysis, recovery of parameters and comparison of algorithms, and robustness to outliers are all discussed showing that the results are insensitive to the choice of priors, efficiency of the algorithm MCMC adopted, and robustness of the model when compared with the beta distribution. Applications to estimate the vulnerability to poverty and to explain the anxiety are performed. The results to applications show that the L-Logistic regression models provide a better fit than the corresponding beta regression models.

Emilia Athayde, Assis Azevedo, Michelli Barros, Víctor Leiva.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 2, 301--328.

Abstract:
The Birnbaum–Saunders (BS) distribution has been largely studied and applied. A random variable with BS distribution is a transformation of another random variable with standard normal distribution. Generalized BS distributions are obtained when the normally distributed random variable is replaced by another symmetrically distributed random variable. This allows us to obtain a wide class of positively skewed models with lighter and heavier tails than the BS model. Its failure rate admits several shapes, including the unimodal case, with its change-point being able to be used for different purposes. For example, to establish the reduction in a dose, and then in the cost of the medical treatment. We analyze the failure rates of generalized BS distributions obtained by the logistic, normal and Student-t distributions, considering their shape and change-point, estimating them, evaluating their robustness, assessing their performance by simulations, and applying the results to real data from different areas.

Alain Desgagné, Philippe Gagnon.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 2, 205--221.

Abstract:
Whole robustness is a nice property to have for statistical models. It implies that the impact of outliers gradually vanishes as they approach plus or minus infinity. So far, the Bayesian literature provides results that ensure whole robustness for the location-scale model. In this paper, we make two contributions. First, we generalise the results to attain whole robustness in simple linear regression through the origin, which is a necessary step towards results for general linear regression models. We allow the variance of the error term to depend on the explanatory variable. This flexibility leads to the second contribution: we provide a simple Bayesian approach to robustly estimate finite population means and ratios. The strategy to attain whole robustness is simple since it lies in replacing the traditional normal assumption on the error term by a super heavy-tailed distribution assumption. As a result, users can estimate the parameters as usual, using the posterior distribution.

Area under ROC curve (AUC) is a widely used performance measure for classification models. We propose two new distributionally robust AUC maximization models (DR-AUC) that rely on the Kantorovich metric and approximate the AUC with the hinge loss function. We consider the two cases with respectively fixed and variable support for the worst-case distribution. We use duality theory to reformulate the DR-AUC models and derive tractable convex optimization problems. The numerical experiments show that the proposed DR-AUC models -- benchmarked with the standard deterministic AUC and the support vector machine models - perform better in general and in particular improve the worst-case out-of-sample performance over the majority of the considered datasets, thereby showing their robustness. The results are particularly encouraging since our numerical experiments are conducted with training sets of small size which have been known to be conducive to low out-of-sample performance.

This paper deals with robust marginal estimation under a general regression model when missing data occur in the response and also in some of covariates. The target is a marginal location parameter which is given through an $M-$functional. To obtain robust Fisher--consistent estimators, properly defined marginal distribution function estimators are considered. These estimators avoid the bias due to missing values by assuming a missing at random condition. Three methods are considered to estimate the marginal distribution function which allows to obtain the $M-$location of interest: the well-known inverse probability weighting, a convolution--based method that makes use of the regression model and an augmented inverse probability weighting procedure that prevents against misspecification. The robust proposed estimators and the classical ones are compared through a numerical study under different missing models including clean and contaminated samples. We illustrate the estimators behaviour under a nonlinear model. A real data set is also analysed.

In causal settings, such as instrumental variable settings, it is well known that estimators based on ordinary least squares (OLS) can yield biased and non-consistent estimates of the causal parameters. This is partially overcome by two-stage least squares (TSLS) estimators. These are, under weak assumptions, consistent but do not have desirable finite sample properties: in many models, for example, they do not have finite moments. The set of K-class estimators can be seen as a non-linear interpolation between OLS and TSLS and are known to have improved finite sample properties. Recently, in causal discovery, invariance properties such as the moment criterion which TSLS estimators leverage have been exploited for causal structure learning: e.g., in cases, where the causal parameter is not identifiable, some structure of the non-zero components may be identified, and coverage guarantees are available. Subsequently, anchor regression has been proposed to trade-off invariance and predictability. The resulting estimator is shown to have optimal predictive performance under bounded shift interventions. In this paper, we show that the concepts of anchor regression and K-class estimators are closely related. Establishing this connection comes with two benefits: (1) It enables us to prove robustness properties for existing K-class estimators when considering distributional shifts. And, (2), we propose a novel estimator in instrumental variable settings by minimizing the mean squared prediction error subject to the constraint that the estimator lies in an asymptotically valid confidence region of the causal parameter. We call this estimator PULSE (p-uncorrelated least squares estimator) and show that it can be computed efficiently, even though the underlying optimization problem is non-convex. We further prove that it is consistent.

Current explanation techniques towards a transparent Convolutional Neural Network (CNN) mainly focuses on building connections between the human-understandable input features with models' prediction, overlooking an alternative representation of the input, the frequency components decomposition. In this work, we present an analysis of the connection between the distribution of frequency components in the input dataset and the reasoning process the model learns from the data. We further provide quantification analysis about the contribution of different frequency components toward the model's prediction. We show that the vulnerability of the model against tiny distortions is a result of the model is relying on the high-frequency features, the target features of the adversarial (black and white-box) attackers, to make the prediction. We further show that if the model develops stronger association between the low-frequency component with true labels, the model is more robust, which is the explanation of why adversarially trained models are more robust against tiny distortions.

John Goes, Gilad Lerman, Boaz Nadler.

Source: The Annals of Statistics, Volume 48, Number 1, 86--110.

Abstract:
Estimating a high-dimensional sparse covariance matrix from a limited number of samples is a fundamental task in contemporary data analysis. Most proposals to date, however, are not robust to outliers or heavy tails. Toward bridging this gap, in this work we consider estimating a sparse shape matrix from $n$ samples following a possibly heavy-tailed elliptical distribution. We propose estimators based on thresholding either Tyler’s M-estimator or its regularized variant. We prove that in the joint limit as the dimension $p$ and the sample size $n$ tend to infinity with $p/n ogamma>0$, our estimators are minimax rate optimal. Results on simulated data support our theoretical analysis.

Gabriela V. Cohen Freue, David Kepplinger, Matías Salibián-Barrera, Ezequiel Smucler.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2065--2090.

Abstract:
In large-scale quantitative proteomic studies, scientists measure the abundance of thousands of proteins from the human proteome in search of novel biomarkers for a given disease. Penalized regression estimators can be used to identify potential biomarkers among a large set of molecular features measured. Yet, the performance and statistical properties of these estimators depend on the loss and penalty functions used to define them. Motivated by a real plasma proteomic biomarkers study, we propose a new class of penalized robust estimators based on the elastic net penalty, which can be tuned to keep groups of correlated variables together in the selected model and maintain robustness against possible outliers. We also propose an efficient algorithm to compute our robust penalized estimators and derive a data-driven method to select the penalty term. Our robust penalized estimators have very good robustness properties and are also consistent under certain regularity conditions. Numerical results show that our robust estimators compare favorably to other robust penalized estimators. Using our proposed methodology for the analysis of the proteomics data, we identify new potentially relevant biomarkers of cardiac allograft vasculopathy that are not found with nonrobust alternatives. The selected model is validated in a new set of 52 test samples and achieves an area under the receiver operating characteristic (AUC) of 0.85.

Chao Gao.

Source: Bernoulli, Volume 26, Number 2, 1139--1170.

Abstract:
This paper studies robust regression in the settings of Huber’s $epsilon$-contamination models. We consider estimators that are maximizers of multivariate regression depth functions. These estimators are shown to achieve minimax rates in the settings of $epsilon$-contamination models for various regression problems including nonparametric regression, sparse linear regression, reduced rank regression, etc. We also discuss a general notion of depth function for linear operators that has potential applications in robust functional linear regression.

Nhat Ho, XuanLong Nguyen, Ya’acov Ritov.

Source: Bernoulli, Volume 26, Number 2, 828--857.

Abstract:
In finite mixture models, apart from underlying mixing measure, true kernel density function of each subpopulation in the data is, in many scenarios, unknown. Perhaps the most popular approach is to choose some kernel functions that we empirically believe our data are generated from and use these kernels to fit our models. Nevertheless, as long as the chosen kernel and the true kernel are different, statistical inference of mixing measure under this setting will be highly unstable. To overcome this challenge, we propose flexible and efficient robust estimators of the mixing measure in these models, which are inspired by the idea of minimum Hellinger distance estimator, model selection criteria, and superefficiency phenomenon. We demonstrate that our estimators consistently recover the true number of components and achieve the optimal convergence rates of parameter estimation under both the well- and misspecified kernel settings for any fixed bandwidth. These desirable asymptotic properties are illustrated via careful simulation studies with both synthetic and real data.

Stanislav Minsker, Xiaohan Wei.

Source: Bernoulli, Volume 26, Number 1, 694--727.

Abstract:
Let $Y$ be a $d$-dimensional random vector with unknown mean $mu $ and covariance matrix $Sigma $. This paper is motivated by the problem of designing an estimator of $Sigma $ that admits exponential deviation bounds in the operator norm under minimal assumptions on the underlying distribution, such as existence of only 4th moments of the coordinates of $Y$. To address this problem, we propose robust modifications of the operator-valued U-statistics, obtain non-asymptotic guarantees for their performance, and demonstrate the implications of these results to the covariance estimation problem under various structural assumptions.

Eduard Belitser, Nurzhan Nurushev.

Source: Bernoulli, Volume 26, Number 1, 191--225.

Abstract:
In the general signal$+$noise (allowing non-normal, non-independent observations) model, we construct an empirical Bayes posterior which we then use for uncertainty quantification for the unknown, possibly sparse, signal. We introduce a novel excessive bias restriction (EBR) condition, which gives rise to a new slicing of the entire space that is suitable for uncertainty quantification. Under EBR and some mild exchangeable exponential moment condition on the noise, we establish the local (oracle) optimality of the proposed confidence ball. Without EBR, we propose another confidence ball of full coverage, but its radius contains an additional $sigma n^{1/4}$-term. In passing, we also get the local optimal results for estimation , posterior contraction problems, and the problem of weak recovery of sparsity structure . Adaptive minimax results (also for the estimation and posterior contraction problems) over various sparsity classes follow from our local results.

Philippe Gagnon, Alain Desgagné, Mylène Bédard.

Source: Bayesian Analysis, Volume 15, Number 2, 389--414.

Abstract:
Linear regression is ubiquitous in statistical analysis. It is well understood that conflicting sources of information may contaminate the inference when the classical normality of errors is assumed. The contamination caused by the light normal tails follows from an undesirable effect: the posterior concentrates in an area in between the different sources with a large enough scaling to incorporate them all. The theory of conflict resolution in Bayesian statistics (O’Hagan and Pericchi (2012)) recommends to address this problem by limiting the impact of outliers to obtain conclusions consistent with the bulk of the data. In this paper, we propose a model with super heavy-tailed errors to achieve this. We prove that it is wholly robust, meaning that the impact of outliers gradually vanishes as they move further and further away from the general trend. The super heavy-tailed density is similar to the normal outside of the tails, which gives rise to an efficient estimation procedure. In addition, estimates are easily computed. This is highlighted via a detailed user guide, where all steps are explained through a simulated case study. The performance is shown using simulation. All required code is given.

Andrea Cremaschi, Raffaele Argiento, Katherine Shoemaker, Christine Peterson, Marina Vannucci.

Source: Bayesian Analysis, Volume 14, Number 4, 1271--1301.

Abstract:
Gaussian graphical models are useful tools for exploring network structures in multivariate normal data. In this paper we are interested in situations where data show departures from Gaussianity, therefore requiring alternative modeling distributions. The multivariate $t$ -distribution, obtained by dividing each component of the data vector by a gamma random variable, is a straightforward generalization to accommodate deviations from normality such as heavy tails. Since different groups of variables may be contaminated to a different extent, Finegold and Drton (2014) introduced the Dirichlet $t$ -distribution, where the divisors are clustered using a Dirichlet process. In this work, we consider a more general class of nonparametric distributions as the prior on the divisor terms, namely the class of normalized completely random measures (NormCRMs). To improve the effectiveness of the clustering, we propose modeling the dependence among the divisors through a nonparametric hierarchical structure, which allows for the sharing of parameters across the samples in the data set. This desirable feature enables us to cluster together different components of multivariate data in a parsimonious way. We demonstrate through simulations that this approach provides accurate graphical model inference, and apply it to a case study examining the dependence structure in radiomics data derived from The Cancer Imaging Atlas.

Mengyang Gu.

Source: Bayesian Analysis, Volume 14, Number 3, 877--905.

Abstract:
Gaussian stochastic process (GaSP) has been widely used in two fundamental problems in uncertainty quantification, namely the emulation and calibration of mathematical models. Some objective priors, such as the reference prior, are studied in the context of emulating (approximating) computationally expensive mathematical models. In this work, we introduce a new class of priors, called the jointly robust prior, for both the emulation and calibration. This prior is designed to maintain various advantages from the reference prior. In emulation, the jointly robust prior has an appropriate tail decay rate as the reference prior, and is computationally simpler than the reference prior in parameter estimation. Moreover, the marginal posterior mode estimation with the jointly robust prior can separate the influential and inert inputs in mathematical models, while the reference prior does not have this property. We establish the posterior propriety for a large class of priors in calibration, including the reference prior and jointly robust prior in general scenarios, but the jointly robust prior is preferred because the calibrated mathematical model typically predicts the reality well. The jointly robust prior is used as the default prior in two new R packages, called “RobustGaSP” and “RobustCalibration”, available on CRAN for emulation and calibration, respectively.

A strong Delaware watermelon season is now under way, with First State melons now reaching customers in grocery stores and markets along the East Coast, from New England to Florida. This season is featuring good yields and excellent quality for Delaware watermelon growers, said Secretary of Agriculture Ed Kee. The First State produces both seeded and seedless watermelon.

ABSTRACT

In Escherichia coli, the chemotaxis response regulator CheY-P binds to FliM, a component of the switch complex at the base of the bacterial flagellar motor, to modulate the direction of motor rotation. The bacterial flagellar motor is ultrasensitive to the concentration of unbound CheY-P in the cytoplasm. CheY-P binds to FliM molecules both in the cytoplasm and on the motor. As the concentration of FliM unavoidably varies from cell to cell, leading to a variation of unbound CheY-P concentration in the cytoplasm, this raises the question whether the flagellar motor is robust against this variation, that is, whether the rotational bias of the motor is more or less constant as the concentration of FliM varies. Here, we showed that the motor is robust against variations of the concentration of FliM. We identified adaptive remodeling of the motor as the mechanism for this robustness. As the level of FliM molecules changes, resulting in different amounts of the unbound CheY-P molecules, the motor adaptively changes the composition of its switch complex to compensate for this effect.

IMPORTANCE The bacterial flagellar motor is an ultrasensitive motor. Its output, the probability of the motor turning clockwise, depends sensitively on the occupancy of the protein FliM (a component on the switch complex of the motor) by the input CheY-P molecules. With a limited cellular pool of CheY-P molecules, cell-to-cell variation of the FliM level would lead to large unwanted variation of the motor output if not compensated. Here, we showed that the motor output is robust against the variation of FliM level and identified the adaptive remodeling of the motor switch complex as the mechanism for this robustness.

ABSTRACT

Hepatitis C virus (HCV) infects millions of people worldwide, causing chronic liver disease that can lead to cirrhosis, hepatocellular carcinoma, and liver transplant. In the last several years, the advent of direct-acting antivirals, including NS3/4A protease inhibitors (PIs), has remarkably improved treatment outcomes of HCV-infected patients. However, selection of resistance-associated substitutions and polymorphisms among genotypes can lead to drug resistance and in some cases treatment failure. A proactive strategy to combat resistance is to constrain PIs within evolutionarily conserved regions in the protease active site. Designing PIs using the substrate envelope is a rational strategy to decrease the susceptibility to resistance by using the constraints of substrate recognition. We successfully designed two series of HCV NS3/4A PIs to leverage unexploited areas in the substrate envelope to improve potency, specifically against resistance-associated substitutions at D168. Our design strategy achieved better resistance profiles over both the FDA-approved NS3/4A PI grazoprevir and the parent compound against the clinically relevant D168A substitution. Crystallographic structural analysis and inhibition assays confirmed that optimally filling the substrate envelope is critical to improve inhibitor potency while avoiding resistance. Specifically, inhibitors that enhanced hydrophobic packing in the S4 pocket and avoided an energetically frustrated pocket performed the best. Thus, the HCV substrate envelope proved to be a powerful tool to design robust PIs, offering a strategy that can be translated to other targets for rational design of inhibitors with improved potency and resistance profiles.

IMPORTANCE Despite significant progress, hepatitis C virus (HCV) continues to be a major health problem with millions of people infected worldwide and thousands dying annually due to resulting complications. Recent antiviral combinations can achieve >95% cure, but late diagnosis, low access to treatment, and treatment failure due to drug resistance continue to be roadblocks against eradication of the virus. We report the rational design of two series of HCV NS3/4A protease inhibitors with improved resistance profiles by exploiting evolutionarily constrained regions of the active site using the substrate envelope model. Optimally filling the S4 pocket is critical to avoid resistance and improve potency. Our results provide drug design strategies to avoid resistance that are applicable to other quickly evolving viral drug targets.

ABSTRACT

The profiling of gene expression by RNA sequencing (RNA-seq) has enabled powerful studies of global transcriptional patterns in all organisms, including bacteria. Because the vast majority of RNA in bacteria is rRNA, it is standard practice to deplete the rRNA from a total RNA sample such that the reads in an RNA-seq experiment derive predominantly from mRNA. One of the most commonly used commercial kits for rRNA depletion, the Ribo-Zero kit from Illumina, was recently discontinued abruptly and for an extended period of time. Here, we report the development of a simple, cost-effective, and robust method for depleting rRNA that can be easily implemented by any lab or facility. We first developed an algorithm for designing biotinylated oligonucleotides that will hybridize tightly and specifically to the 23S, 16S, and 5S rRNAs from any species of interest. Precipitation of these oligonucleotides bound to rRNA by magnetic streptavidin-coated beads then depletes rRNA from a complex, total RNA sample such that ~75 to 80% of reads in a typical RNA-seq experiment derive from mRNA. Importantly, we demonstrate a high correlation of RNA abundance or fold change measurements in RNA-seq experiments between our method and the Ribo-Zero kit. Complete details on the methodology are provided, including open-source software for designing oligonucleotides optimized for any bacterial species or community of interest.

IMPORTANCE The ability to examine global patterns of gene expression in microbes through RNA sequencing has fundamentally transformed microbiology. However, RNA-seq depends critically on the removal of rRNA from total RNA samples. Otherwise, rRNA would comprise upward of 90% of the reads in a typical RNA-seq experiment, limiting the reads coming from mRNA or requiring high total read depth. A commonly used kit for rRNA subtraction from Illumina was recently unavailable for an extended period of time, disrupting routine rRNA depletion. Here, we report the development of a "do-it-yourself" kit for rapid, cost-effective, and robust depletion of rRNA from total RNA. We present an algorithm for designing biotinylated oligonucleotides that will hybridize to the rRNAs from a target set of species. We then demonstrate that the designed oligonucleotides enable sufficient rRNA depletion to produce RNA-seq data with 75 to 80% of reads coming from mRNA. The methodology presented should enable RNA-seq studies on any species or metagenomic sample of interest.

Capture bioprocessing unit operations were previously shown to clear or kill several log₁₀ of a model mycoplasma Acholeplasma laidlawii in lab-scale spike/removal studies. Here, we confirm this observation with two additional mollicute species relevant to biotechnology products for human use: Mycoplasma orale and Mycoplasma arginini. Clearance of M. orale and M. arginini from protein A column purification was similar to that seen with A. laidlawii, though some between cycle carryover was evident, especially for M. orale. However, on-resin growth studies for all three species revealed that residual mycoplasma in a column slowly die off over time rather than expanding further. Solvent/detergent exposure completely inactivated M. arginini though detectable levels of M. orale remained. A small-scale model of a commercial low-pH hold step did inactivate live M. orale, but this inactivation required a lower pH set point and occurred with slower kinetics than previously seen with A. laidlawii. Additionally, ultraviolet-C irradiation was shown to be effective for A. laidlawii and M. orale inactivation whereas virus-retentive filters for upstream and downstream processes, as expected, cleared A. laidlawii. These data argue that M. orale and M. arginini overall would be largely cleared by early bioprocessing steps as shown previously for A. laidlawii, and that barrier technologies can effectively reduce the risk from media components. For some unit operations, M. orale and M. arginini may be hardier, and require more stringent processing or equipment cleaning conditions to assure effective mycoplasma reduction. By exploring how some of the failure modes in commercial antibody manufacturing processes can still eliminate mycoplasma burden, we demonstrate that required best practices assure biotechnology products will be safe for patients.

Yokogawa Electric Corporation (TOKYO: 6841) announces the release of artificial intelligence (AI)-enabled versions of the GX series panel-mount type paperless recorders, GP series portable paperless recorders, and GA10 data logging software, which are components of the highly operable and expandable SMARTDAC+data acquisition and control system. This new AI functionality includes the future pen, a function developed by Yokogawa that enables the drawing of predicted waveforms. Yokogawa is also releasing a new CPU module for the e-RT3 Plus edge computing platform that is environmentally robust and Python compatible. The GX/GP and e-RT3 release is set for April 8, and the GA10 software will be released on May 13. The SMARTDAC+ system is a product in the OpreX Data Acquisition family, and the e-RT3 Plus is part of the OpreX Control Devices family.

Federal Reserve Chairman Jerome Powell said more stimulus is needed to ensure a robust economic recovery from the coronavirus crisis.

I was literally confused to deployment testing services and testing scenarios, but thank to my one friend who advised me to use the computaris “top testing suite”. It was the...

Extensive structural reforms since the early 1990s have strengthened the resilience of the Swedish economy to shocks.

This paper provides a framework for comparing a defined benefit (DB) and a defined contribution (DC) point schemes, which are both pay-as-you go (PAYG) financed.

Policymakers need to deploy broad-based reform plans that incorporate monetary, fiscal, and structural policies to stimulate persistently weak demand, re-launch productivity growth, create jobs and build a more inclusive global economy, according to the OECD’s annual Going for Growth report.

Successful implementation of economic reforms has boosted the Latvian economy, leading to strong growth, rising wages and solid public finances. Further policy action is now needed to accelerate productivity growth, create jobs, drive down poverty, improve living standards and ensure that everyone benefits from more inclusive growth, according to a new report from the OECD.

The recent droughts in Brazil’s Rio de Janeiro and São Paulo states have exposed the need to shift from crisis management to effective risk governance of the country’s water resources, according to a new OECD report.

The recent droughts in Brazil’s Rio de Janeiro and São Paulo states have exposed the need to shift from crisis management to effective risk governance of the country’s water resources, according to a new OECD report.

Earnings of $3.6bn exceeded expectations ahead of collapse in global oil demand

A UK parliamentary committee that focuses on human rights issues has called for primary legislation to be put in place to ensure that legal protections wrap around the national coronavirus contact tracing app. The app, called NHS COVID-19, is being fast tracked for public use — with a test ongoing this week in the Isle […]

Rodwell, who played his last game for Sunderland ten months ago, has signed a deal with The Championship club until the end of the season in a bid to get his career back on track.