Carla E. Cox
Aug 1, 2017; 30:157-160
From Research to Practice

Marion J. Franz
Jul 1, 2007; 20:133-136
Editorials

VOLUME 285 (2010) PAGES 13742–13747In Fig. 1E, passage 10, the splicing of a non-adjacent lane from the same immunoblot was not marked. This error has now been corrected and does not affect the results or conclusions of this work.jbc;295/16/5533/F1F1F1Figure 1E.

(Impact Journals LLC) Oncotarget Volume 11, Issue 12 reported that the p16 tumor suppressor is coded by CDKN2A and plays an important role during carcinogenesis and tumor progression in numerous tumor entities.

Every summer, rainstorms occur in Hong Kong occasionally, leading to loss of property or even casualties.

(American Association for the Advancement of Science) Ice sheet losses from Greenland and Antarctica have outpaced snow accumulation and contributed approximately 14 millimeters to sea level rise over 16 years (2003 to 2019), a new analysis of data from NASA's laser-shooting satellites has revealed.

Samuel W Entwisle
Apr 6, 2020; 0:RA120.001946v2-mcp.RA120.001946
Research

Stimulating brown adipose tissue (BAT) activity represents a promising therapy for overcoming metabolic diseases. mTORC2 is important for regulating BAT metabolism, but its downstream targets have not been fully characterized. In this study, we apply proteomics and phosphoproteomics to investigate the downstream effectors of mTORC2 in brown adipocytes. We compare wild-type controls to isogenic cells with an induced knockout of the mTORC2 subunit RICTOR (Rictor-iKO) by stimulating each with insulin for a 30-minute time course. In Rictor-iKO cells, we identify decreases to the abundance of glycolytic and de novo lipogenesis enzymes, and increases to mitochondrial proteins as well as a set of proteins known to increase upon interferon stimulation. We also observe significant differences to basal phosphorylation due to chronic RICTOR loss including decreased phosphorylation of the lipid droplet protein perilipin-1 in Rictor-iKO cells, suggesting that RICTOR could be involved with regulating basal lipolysis or droplet dynamics. Finally, we observe mild dampening of acute insulin signaling response in Rictor-iKO cells, and a subset of AKT substrates exhibiting statistically significant dependence on RICTOR.

When Tyler Glasnow heard the news that the Rays were signing Charlie Morton, he couldn't help but get even more excited for the 2019 season.

VOLUME 285 (2010) PAGES 13742–13747In Fig. 1E, passage 10, the splicing of a non-adjacent lane from the same immunoblot was not marked. This error has now been corrected and does not affect the results or conclusions of this work.jbc;295/16/5533/F1F1F1Figure 1E.

Obat Pelangsing Badan Herbal Fatloss Jimpness Beauty Penghilang Lemak Seketika Merupakan Obat Pelangsing Badan Yang Aman Dikonsumsi Untuk Pria Maupun Wanita

REGGAE ARTISTE Isha Blender is still coming to terms with the loss of her son, Josiah, on January 5, a mere two days after she celebrated her birthday. The daughter of legendary crooner Everton Blender said the death of a child can be one of the...

Many older adults have difficulty understanding speech in acute healthcare settings owing to hearing loss, but the effect on patient care is often overlooked. Jan Blustein professor of health policy and medicine at New York University, and who has also experienced the affects of hearing loss, joins us to explain what that's like, and gives some...

Jon Miller is having trouble believing that baseball legend Frank Robinson is gone. Robinson, 83, passed away Thursday morning.

Jens J. Holst
May 1, 2011; 34:S251-S257
Diabetes Treatments

Anastassios G. Pittas
Dec 1, 2005; 28:2939-2941
BR Clinical Care/Education/Nutrition

Jordi Salas-Salvadó
May 1, 2019; 42:777-788
Continuing Evolution of Nutritional Therapy for Diabetes

OBJECTIVE

To assess functional β-cell capacity in type 2 diabetes during 2 years of remission induced by dietary weight loss.

OBJECTIVE

Corneal nerve fiber length (CNFL) represents a biomarker for diabetic distal symmetric polyneuropathy (DSP). We aimed to determine the reference distribution of annual CNFL change, the prevalence of abnormal change in diabetes, and its associated clinical variables.

OBJECTIVE

We examined detailed renal and cardiovascular (CV) outcomes after gastric bypass (GBP) surgery in people with obesity and type 2 diabetes mellitus (T2DM), across several renal function categories, in a nationwide cohort study.

OBJECTIVE

To examine the effect of combination therapy with canagliflozin plus liraglutide on HbA_1c, endogenous glucose production (EGP), and body weight versus each therapy alone.

The United States' bellwether stock index, the Dow Jones Industrial Average, climbed another 455 points Friday, posting its first weekly gain in three weeks.

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.

The food lowers cravings for high-sugar and high-fat foods and suppresses appetite during the day.

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Coronavirus is spreading rapidly in pre- and post-trial correctional facilities across the United States, and the challenges of social distancing for students in regular districts are all massively compounded for students behind bars.

Along with winter vegetables, STEM is blooming in Imperial County. Dennis and Daniel Gibbs are growing young scientists by transplanting the scientific method to the second grade.

State Assembly member Tony Thurmond ultimately prevailed over former charter school executive Marshall Tuck in a contest that drew more than $50 million in outside campaign spending.

London : Elliot Stock, 1886.

Paris : V.A. Delahaye, 1878.

Edinburgh : Bell & Bradfute, 1807.

Mississippi’s outdated textbooks teach an abbreviated version of civil rights, undermining the state’s new ‘innovative’ standards.

2005.

Yichong Wu, Tiejun Li, Xiaoping Liu, Luonan Chen.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1269--1301.

Abstract:
Detecting the change of biological interaction networks is of great importance in biological and medical research. We proposed a simple loss function, named as CrossFDTL, to identify the network change or differential network by estimating the difference between two precision matrices under Gaussian assumption. The CrossFDTL is a natural fusion of the D-trace loss for the considered two networks by imposing the $ell _{1}$ penalty to the differential matrix to ensure sparsity. The key point of our method is to utilize the cross variables, which correspond to the sum and difference of two precision matrices instead of using their original forms. Moreover, we developed an efficient minimization algorithm for the proposed loss function and further rigorously proved its convergence. Numerical results showed that our method outperforms the existing methods in both accuracy and convergence speed for the simulated and real data.

Over the past decades, numerous loss functions have been been proposed for a variety of supervised learning tasks, including regression, classification, ranking, and more generally structured prediction. Understanding the core principles and theoretical properties underpinning these losses is key to choose the right loss for the right problem, as well as to create new losses which combine their strengths. In this paper, we introduce Fenchel-Young losses, a generic way to construct a convex loss function for a regularized prediction function. We provide an in-depth study of their properties in a very broad setting, covering all the aforementioned supervised learning tasks, and revealing new connections between sparsity, generalized entropies, and separation margins. We show that Fenchel-Young losses unify many well-known loss functions and allow to create useful new ones easily. Finally, we derive efficient predictive and training algorithms, making Fenchel-Young losses appealing both in theory and practice.

We present new excess risk bounds for general unbounded loss functions including log loss and squared loss, where the distribution of the losses may be heavy-tailed. The bounds hold for general estimators, but they are optimized when applied to $eta$-generalized Bayesian, MDL, and empirical risk minimization estimators. In the case of log loss, the bounds imply convergence rates for generalized Bayesian inference under misspecification in terms of a generalization of the Hellinger metric as long as the learning rate $eta$ is set correctly. For general loss functions, our bounds rely on two separate conditions: the $v$-GRIP (generalized reversed information projection) conditions, which control the lower tail of the excess loss; and the newly introduced witness condition, which controls the upper tail. The parameter $v$ in the $v$-GRIP conditions determines the achievable rate and is akin to the exponent in the Tsybakov margin condition and the Bernstein condition for bounded losses, which the $v$-GRIP conditions generalize; favorable $v$ in combination with small model complexity leads to $ ilde{O}(1/n)$ rates. The witness condition allows us to connect the excess risk to an 'annealed' version thereof, by which we generalize several previous results connecting Hellinger and Rényi divergence to KL divergence.

Mohd Arshad, Omer Abdalghani.

Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 1, 167--182.

Abstract:
Suppose that $pi_{1},pi_{2},ldots ,pi_{k}$ be $k(geq2)$ independent exponential populations having unknown location parameters $mu_{1},mu_{2},ldots,mu_{k}$ and known scale parameters $sigma_{1},ldots,sigma_{k}$. Let $mu_{[k]}=max {mu_{1},ldots,mu_{k}}$. For selecting the population associated with $mu_{[k]}$, a class of selection rules (proposed by Arshad and Misra [ Statistical Papers 57 (2016) 605–621]) is considered. We consider the problem of estimating the location parameter $mu_{S}$ of the selected population under the criterion of the LINEX loss function. We consider three natural estimators $delta_{N,1},delta_{N,2}$ and $delta_{N,3}$ of $mu_{S}$, based on the maximum likelihood estimators, uniformly minimum variance unbiased estimator (UMVUE) and minimum risk equivariant estimator (MREE) of $mu_{i}$’s, respectively. The uniformly minimum risk unbiased estimator (UMRUE) and the generalized Bayes estimator of $mu_{S}$ are derived. Under the LINEX loss function, a general result for improving a location-equivariant estimator of $mu_{S}$ is derived. Using this result, estimator better than the natural estimator $delta_{N,1}$ is obtained. We also shown that the estimator $delta_{N,1}$ is dominated by the natural estimator $delta_{N,3}$. Finally, we perform a simulation study to evaluate and compare risk functions among various competing estimators of $mu_{S}$.

Abbas Pak, M. E. Ghitany, Mohammad Reza Mahmoudi.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 4, 894--914.

Abstract:
This paper focuses on Bayesian estimation of the parameters and reliability function of the power Lindley distribution by using various symmetric and asymmetric loss functions. Assuming suitable priors on the parameters, Bayes estimates are derived by using squared error, linear exponential (linex) and general entropy loss functions. Since, under these loss functions, Bayes estimates of the parameters do not have closed forms we use lindley’s approximation technique to calculate the Bayes estimates. Moreover, we obtain the Bayes estimates of the parameters using a Markov Chain Monte Carlo (MCMC) method. Simulation studies are conducted in order to evaluate the performances of the proposed estimators under the considered loss functions. Finally, analysis of a real data set is presented for illustrative purposes.

Introducing common shocks is a popular dependence modelling approach, with some recent applications in loss reserving. The main advantage of this approach is the ability to capture structural dependence coming from known relationships. In addition, it helps with the parsimonious construction of correlation matrices of large dimensions. However, complications arise in the presence of "unbalanced data", that is, when (expected) magnitude of observations over a single triangle, or between triangles, can vary substantially. Specifically, if a single common shock is applied to all of these cells, it can contribute insignificantly to the larger values and/or swamp the smaller ones, unless careful adjustments are made. This problem is further complicated in applications involving negative claim amounts. In this paper, we address this problem in the loss reserving context using a common shock Tweedie approach for unbalanced data. We show that the solution not only provides a much better balance of the common shock proportions relative to the unbalanced data, but it is also parsimonious. Finally, the common shock Tweedie model also provides distributional tractability.

People learn to discriminate between classes without explicit exposure to negative examples. On the contrary, traditional machine learning algorithms often rely on negative examples, otherwise the model would be prone to collapse and always-true predictions. Therefore, it is crucial to design the learning objective which leads the model to converge and to perform predictions unbiasedly without explicit negative signals. In this paper, we propose a Collectively loss function to learn from only Positive and Unlabeled data (cPU). We theoretically elicit the loss function from the setting of PU learning. We perform intensive experiments on the benchmark and real-world datasets. The results show that cPU consistently outperforms the current state-of-the-art PU learning methods.

Members of the future American workforce could see losses of earnings that add up to trillions of dollars, depending on how long coronavirus-related school closures persist.

The post Economists Expect Huge Future Earnings Loss for Students Missing School Due to COVID-19 appeared first on Market Brief.

Cristiano Villa, Jeong Eun Lee.

Source: Bayesian Analysis, Volume 15, Number 2, 533--558.

Abstract:
In this work we propose a novel model prior for variable selection in linear regression. The idea is to determine the prior mass by considering the worth of each of the regression models, given the number of possible covariates under consideration. The worth of a model consists of the information loss and the loss due to model complexity. While the information loss is determined objectively, the loss expression due to model complexity is flexible and, the penalty on model size can be even customized to include some prior knowledge. Some versions of the loss-based prior are proposed and compared empirically. Through simulation studies and real data analyses, we compare the proposed prior to the Scott and Berger prior, for noninformative scenarios, and with the Beta-Binomial prior, for informative scenarios.

Adam J. Krause
Mar 20, 2019; 39:2291-2300
BehavioralSystemsCognitive

Ben Warren
Apr 8, 2020; 40:3130-3140
Neurobiology of Disease

Michele Bellesi
May 24, 2017; 37:5263-5273
Cellular