Many chemicals and cellular processes cause oxidative stress that can damage lipids, proteins, or DNA (1). To quickly sense and respond to this ubiquitous threat, organisms have evolved enzymes that neutralize harmful oxidants such as reactive oxygen species and electrophilic compounds (including xenobiotics and their breakdown products) in cells.These antioxidant enzymes include GSH S-transferase (GST),2 NADPH:quinone oxidoreductase 1, thioredoxin, hemeoxygenase-1, and others (2, 3). Many of these proteins are commonly expressed in cells exposed to oxidative stress.The antioxidant response element (ARE) is a major regulatory component of this cellular stress response. The ARE is a conserved, 11-nucleotide-long DNA motif present in the 5'-flanking regions of many genes encoding antioxidant proteins. The laboratory of Cecil Pickett (Fig. 1) at the Merck Frosst Centre for Therapeutic Research in Quebec discovered ARE, a finding reported in the early 1990s in two JBC papers recognized as Classics here (4, 5).jbc;295/12/3929/F1F1F1Figure 1.Cecil Pickett (pictured) and colleagues first described the ARE motif, present in the 5' regions of many genes whose expression is up-regulated by oxidative stress and xenobiotics. Photo courtesy of Cecil Pickett.ARE's discovery was spurred in large part by Pickett's career choice. After completing a PhD in biology and a 2-year postdoc at UCLA in the mid-1970s, he began to work in the pharmaceutical industry.Recruited to Merck in 1978 by its then head of research and development (and later CEO), Roy Vagelos, “I became interested in how drug-metabolizing enzymes were induced by various xenobiotics,” Pickett says.According to Pickett, Vagelos encouraged researchers at the company...

7 September 2012 , Volume 88, Number 5

The BMJ in partnership with The Harvard Global Health Institute has launched a collection of articles exploring how to achieve effective universal health coverage (UHC). The collection highlights the importance of quality in UHC, potential finance models, how best to incentivise stakeholders, and some of the barriers to true UHC. One of those...

The American Dental Association sent a letter to third-party payers urging that administrators of dental benefit plans adjust and adapt reimbursement procedures important to dentists and patients — including coverage for temporary procedures and adjusting fee schedules to account for cost of increasing infection control procedures ¬— in the midst of the “unprecedented and extraordinary circumstances dentists and their patients face” during the pandemic.

The ADA is streaming a free webinar April 14 intended to help dentists jump-start their recovery process during and after the COVID-19 pandemic.

American Dental Association President Chad P. Gehani has assembled an advisory task force to oversee the ADA’s development of tools for dentists as they bounce back from the effects of practice restrictions and closures caused by the COVID-19 pandemic.

Association staff and members of the ADA Advisory Task Force on Dental Practice Recovery are aware and working diligently in addressing members’ concerns over the limited availability of certain personal protective equipment items.

A microbe found in mosquitoes that appears to block malaria could be used to control spread of the disease in humans, according to researchers in Kenya and Britain.

Televangelist Jim Bakker is recovering at his North Carolina home after having a stroke, his wife said Friday.

WASHINGTON — Latinos and immigrants are at least twice as likely to lack health insurance coverage as the overall population in three central Kansas City metro counties, a new Migration Policy Institute (MPI) study reveals. In fact, they are four times as likely to be uninsured in Johnson County, Kansas. 

WASHINGTON – As more than 33 million U.S. workers have lost their jobs since March amid the pandemic-induced economic crisis, immigrants are among the most vulnerable: They are more likely than the U.S. born to be laid off and to live in communities with high COVID-19 infection rates, and less likely to have health insurance coverage and access to a doctor or other usual source of health care.

Latinos and immigrants are at least twice as likely to lack health insurance coverage as the overall population in the Kansas City metropolitan area. This gap that has significant implications for the region, as Latinos and immigrants will form an ever-growing share of the area’s labor force and tax base amid anticipated declines in the native-born, non-Latino population.

As millions of U.S. workers lose jobs and the health insurance associated with them, Medicaid and similar programs are increasingly important for people seeking COVID-19 testing and treatment. Yet many low-income uninsured noncitizens, including green-card holders, are excluded from such programs because of their immigration status, as this fact sheet explores.

An essential mineral in the body have been linked to recovery of COVID-19 patients.

→ Support PsyBlog for just $5 per month. Enables access to articles marked (M) and removes ads.

→ Explore PsyBlog's ebooks, all written by Dr Jeremy Dean:

• Accept Yourself: How to feel a profound sense of warmth and self-compassion
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Although mental health is just as important as physical health in promoting overall well-being, many insurance companies in the past did not agree with that viewpoint. This is shown by the fact that, for many years,  a large percentage of insurers provided better insurance coverage for physical issues than mental health issues. However, in 2008, […]

What's your passion? Social entrepreneur Noeline Kirabo reflects on her work helping out-of-school young people in Uganda turn their passions into profitable businesses -- and shares the two questions you can ask yourself to begin doing the same.

Before the coronavirus pandemic, bioengineer Jim Collins and his team combined the power of AI with synthetic biology in an effort to combat a different looming crisis: antibiotic-resistant superbugs. Collins explains how they pivoted their efforts to begin developing a series of tools and antiviral compounds to help fight COVID-19 -- and shares their plan to discover seven new classes of antibiotics over the next seven years. (This ambitious plan is a part of The Audacious Project, TED's initiative to inspire and fund global change.)

A great animated video from the Youth Council of the Mental Health Commission of Canada, about what it means to put recovery principles to practice for youth.

U.S. Secretary of Education Betsy DeVos on Wednesday visited a Kentucky high school that is recovering from a 2018 shooting to award additional grant money meant to aid its recovery efforts.

The Regional Recycling Transport Assistance Package provides funding to support resource recovery and recycling in regional Queensland, helping fund the costs of transporting recyclable material from regional Queensland to facilities where it can be recovered or processed and turned into new products. Details regarding eligible applicants, projects and costs are provided in these guidelines.

London : printed for B. and J. White, 1797.

London : Elliot Stock, 1886.

London : J. Churchill, 1839.

London : John Bale, 1897.

Resurrecting an old idea about assessing school quality could allow schools to examine a broad range of data on performance and practices and lead to improvement.

[London?], [between 1700 and 1799?]

Rockville, Maryland : National Institute on Drug Abuse, 1986.

York : York Consulting, 2001.

Hear how former Oregon State guard and current member of the WNBA's LA Sparks Sydney Wiese is recovering from a COVID-19 diagnosis, seeing friends and family show support and love during a trying time.

Pac-12 Networks' Mike Yam interviews former Oregon State guard Sydney Wiese to hear how she's recovering from contracting COVID-19. Wiese recounts her recent travel and how she's been lifted up by steadfast support from friends, family and fellow WNBA players. See more from Wiese during "Pac-12 Playlist" on Monday, April 6 at 7 p.m. PT/ 8 p.m. MT on Pac-12 Network.

Ming Yu, Varun Gupta, Mladen Kolar.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 413--457.

Abstract:
We study the problem of recovery of matrices that are simultaneously low rank and row and/or column sparse. Such matrices appear in recent applications in cognitive neuroscience, imaging, computer vision, macroeconomics, and genetics. We propose a GDT (Gradient Descent with hard Thresholding) algorithm to efficiently recover matrices with such structure, by minimizing a bi-convex function over a nonconvex set of constraints. We show linear convergence of the iterates obtained by GDT to a region within statistical error of an optimal solution. As an application of our method, we consider multi-task learning problems and show that the statistical error rate obtained by GDT is near optimal compared to minimax rate. Experiments demonstrate competitive performance and much faster running speed compared to existing methods, on both simulations and real data sets.

Matthias Löwe, Kristina Schubert.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1796--1815.

Abstract:
We show how to exactly reconstruct the block structure at the critical line in the so-called Ising block model. This model was recently re-introduced by Berthet, Rigollet and Srivastava in [2]. There the authors show how to exactly reconstruct blocks away from the critical line and they give an upper and a lower bound on the number of observations one needs; thereby they establish a minimax optimal rate (up to constants). Our technique relies on a combination of their methods with fluctuation results obtained in [20]. The latter are extended to the full critical regime. We find that the number of necessary observations depends on whether the interaction parameter between two blocks is positive or negative: In the first case, there are about $Nlog N$ observations required to exactly recover the block structure, while in the latter case $sqrt{N}log N$ observations suffice.

Sihai Dave Zhao, Yet Tien Nguyen.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 110--142.

Abstract:
It is frequently of interest to identify simultaneous signals, defined as features that exhibit statistical significance across each of several independent experiments. For example, genes that are consistently differentially expressed across experiments in different animal species can reveal evolutionarily conserved biological mechanisms. However, in some problems the test statistics corresponding to these features can have complicated or unknown null distributions. This paper proposes a novel nonparametric false discovery rate control procedure that can identify simultaneous signals even without knowing these null distributions. The method is shown, theoretically and in simulations, to asymptotically control the false discovery rate. It was also used to identify genes that were both differentially expressed and proximal to differentially accessible chromatin in the brains of mice exposed to a conspecific intruder. The proposed method is available in the R package github.com/sdzhao/ssa.

This paper presents a new open source Python framework for causal discovery from observational data and domain background knowledge, aimed at causal graph and causal mechanism modeling. The cdt package implements an end-to-end approach, recovering the direct dependencies (the skeleton of the causal graph) and the causal relationships between variables. It includes algorithms from the `Bnlearn' and `Pcalg' packages, together with algorithms for pairwise causal discovery such as ANM.

Increasing number of sectors which affect human lives, are using Machine Learning (ML) tools. Hence the need for understanding their working mechanism and evaluating their fairness in decision-making, are becoming paramount, ushering in the era of Explainable AI (XAI). In this contribution we introduced a few intrinsically interpretable models which are also capable of dealing with missing values, in addition to extracting knowledge from the dataset and about the problem. These models are also capable of visualisation of the classifier and decision boundaries: they are the angle based variants of Learning Vector Quantization. We have demonstrated the algorithms on a synthetic dataset and a real-world one (heart disease dataset from the UCI repository). The newly developed classifiers helped in investigating the complexities of the UCI dataset as a multiclass problem. The performance of the developed classifiers were comparable to those reported in literature for this dataset, with additional value of interpretability, when the dataset was treated as a binary class problem.

Florentina Bunea, Christophe Giraud, Xi Luo, Martin Royer, Nicolas Verzelen.

Source: The Annals of Statistics, Volume 48, Number 1, 111--137.

Abstract:
The problem of variable clustering is that of estimating groups of similar components of a $p$-dimensional vector $X=(X_{1},ldots ,X_{p})$ from $n$ independent copies of $X$. There exists a large number of algorithms that return data-dependent groups of variables, but their interpretation is limited to the algorithm that produced them. An alternative is model-based clustering, in which one begins by defining population level clusters relative to a model that embeds notions of similarity. Algorithms tailored to such models yield estimated clusters with a clear statistical interpretation. We take this view here and introduce the class of $G$-block covariance models as a background model for variable clustering. In such models, two variables in a cluster are deemed similar if they have similar associations will all other variables. This can arise, for instance, when groups of variables are noise corrupted versions of the same latent factor. We quantify the difficulty of clustering data generated from a $G$-block covariance model in terms of cluster proximity, measured with respect to two related, but different, cluster separation metrics. We derive minimax cluster separation thresholds, which are the metric values below which no algorithm can recover the model-defined clusters exactly, and show that they are different for the two metrics. We therefore develop two algorithms, COD and PECOK, tailored to $G$-block covariance models, and study their minimax-optimality with respect to each metric. Of independent interest is the fact that the analysis of the PECOK algorithm, which is based on a corrected convex relaxation of the popular $K$-means algorithm, provides the first statistical analysis of such algorithms for variable clustering. Additionally, we compare our methods with another popular clustering method, spectral clustering. Extensive simulation studies, as well as our data analyses, confirm the applicability of our approach.

Keisuke Yano, Kengo Kato.

Source: Bernoulli, Volume 26, Number 1, 616--641.

Abstract:
In this paper, we study frequentist coverage errors of Bayesian credible sets for an approximately linear regression model with (moderately) high dimensional regressors, where the dimension of the regressors may increase with but is smaller than the sample size. Specifically, we consider quasi-Bayesian inference on the slope vector under the quasi-likelihood with Gaussian error distribution. Under this setup, we derive finite sample bounds on frequentist coverage errors of Bayesian credible rectangles. Derivation of those bounds builds on a novel Berry–Esseen type bound on quasi-posterior distributions and recent results on high-dimensional CLT on hyperrectangles. We use this general result to quantify coverage errors of Castillo–Nickl and $L^{infty}$-credible bands for Gaussian white noise models, linear inverse problems, and (possibly non-Gaussian) nonparametric regression models. In particular, we show that Bayesian credible bands for those nonparametric models have coverage errors decaying polynomially fast in the sample size, implying advantages of Bayesian credible bands over confidence bands based on extreme value theory.