Elevated pressure in the pancreatic gland is the central cause of pancreatitis following abdominal trauma, surgery, endoscopic retrograde cholangiopancreatography, and gallstones. In the pancreas, excessive intracellular calcium causes mitochondrial dysfunction, premature zymogen activation, and necrosis, ultimately leading to pancreatitis. Although stimulation of the mechanically activated, calcium-permeable ion channel Piezo1 in the pancreatic acinar cell is the initial step in pressure-induced pancreatitis, activation of Piezo1 produces only transient elevation in intracellular calcium that is insufficient to cause pancreatitis. Therefore, how pressure produces a prolonged calcium elevation necessary to induce pancreatitis is unknown. We demonstrate that Piezo1 activation in pancreatic acinar cells caused a prolonged elevation in intracellular calcium levels, mitochondrial depolarization, intracellular trypsin activation, and cell death. Notably, these effects were dependent on the degree and duration of force applied to the cell. Low or transient force was insufficient to activate these pathological changes, whereas higher and prolonged application of force triggered sustained elevation in intracellular calcium, leading to enzyme activation and cell death. All of these pathological events were rescued in acinar cells treated with a Piezo1 antagonist and in acinar cells from mice with genetic deletion of Piezo1. We discovered that Piezo1 stimulation triggered transient receptor potential vanilloid subfamily 4 (TRPV4) channel opening, which was responsible for the sustained elevation in intracellular calcium that caused intracellular organelle dysfunction. Moreover, TRPV4 gene–KO mice were protected from Piezo1 agonist– and pressure-induced pancreatitis. These studies unveil a calcium signaling pathway in which a Piezo1-induced TRPV4 channel opening causes pancreatitis.

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Official counts of COVID-19 deaths have been criticized for potentially including people who did not die of COVID-19 but merely died with COVID-19. I address that critique by fitting a generalized additive model to weekly counts of all registered deaths in England and Wales during the 2010s. The model produces baseline rates of death registrations expected in the absence of the COVID-19 pandemic, and comparing those baselines to recent counts of registered deaths exposes the emergence of excess deaths late in March 2020. Among adults aged 45+, about 38,700 excess deaths were registered in the 5 weeks comprising 21 March through 24 April (612 $pm$ 416 from 21$-$27 March, 5675 $pm$ 439 from 28 March through 3 April, then 9183 $pm$ 468, 12,712 $pm$ 589, and 10,511 $pm$ 567 in April's next 3 weeks). Both the Office for National Statistics's respective count of 26,891 death certificates which mention COVID-19, and the Department of Health and Social Care's hospital-focused count of 21,222 deaths, are appreciably less, implying that their counting methods have underestimated rather than overestimated the pandemic's true death toll. If underreporting rates have held steady, about 45,900 direct and indirect COVID-19 deaths might have been registered by April's end but not yet publicly reported in full.

A major difficulty of solving continuous POMDPs is to infer the multi-modal distribution of the unobserved true states and to make the planning algorithm dependent on the perceived uncertainty. We cast POMDP filtering and planning problems as two closely related Sequential Monte Carlo (SMC) processes, one over the real states and the other over the future optimal trajectories, and combine the merits of these two parts in a new model named the DualSMC network. In particular, we first introduce an adversarial particle filter that leverages the adversarial relationship between its internal components. Based on the filtering results, we then propose a planning algorithm that extends the previous SMC planning approach [Piche et al., 2018] to continuous POMDPs with an uncertainty-dependent policy. Crucially, not only can DualSMC handle complex observations such as image input but also it remains highly interpretable. It is shown to be effective in three continuous POMDP domains: the floor positioning domain, the 3D light-dark navigation domain, and a modified Reacher domain.

Boosting is one of the most successful ideas in machine learning. The most well-accepted explanations for the low generalization error of boosting algorithms such as AdaBoost stem from margin theory. The study of margins in the context of boosting algorithms was initiated by Schapire, Freund, Bartlett and Lee (1998) and has inspired numerous boosting algorithms and generalization bounds. To date, the strongest known generalization (upper bound) is the $k$th margin bound of Gao and Zhou (2013). Despite the numerous generalization upper bounds that have been proved over the last two decades, nothing is known about the tightness of these bounds. In this paper, we give the first margin-based lower bounds on the generalization error of boosted classifiers. Our lower bounds nearly match the $k$th margin bound and thus almost settle the generalization performance of boosted classifiers in terms of margins.

Adaptive importance samplers are adaptive Monte Carlo algorithms to estimate expectations with respect to some target distribution which extit{adapt} themselves to obtain better estimators over a sequence of iterations. Although it is straightforward to show that they have the same $mathcal{O}(1/sqrt{N})$ convergence rate as standard importance samplers, where $N$ is the number of Monte Carlo samples, the behaviour of adaptive importance samplers over the number of iterations has been left relatively unexplored. In this work, we investigate an adaptation strategy based on convex optimisation which leads to a class of adaptive importance samplers termed extit{optimised adaptive importance samplers} (OAIS). These samplers rely on the iterative minimisation of the $chi^2$-divergence between an exponential-family proposal and the target. The analysed algorithms are closely related to the class of adaptive importance samplers which minimise the variance of the weight function. We first prove non-asymptotic error bounds for the mean squared errors (MSEs) of these algorithms, which explicitly depend on the number of iterations and the number of samples together. The non-asymptotic bounds derived in this paper imply that when the target belongs to the exponential family, the $L_2$ errors of the optimised samplers converge to the optimal rate of $mathcal{O}(1/sqrt{N})$ and the rate of convergence in the number of iterations are explicitly provided. When the target does not belong to the exponential family, the rate of convergence is the same but the asymptotic $L_2$ error increases by a factor $sqrt{ ho^star} > 1$, where $ ho^star - 1$ is the minimum $chi^2$-divergence between the target and an exponential-family proposal.

The Rosenbrock function is an ubiquitous benchmark problem for numerical optimisation, and variants have been proposed to test the performance of Markov Chain Monte Carlo algorithms. In this work we discuss the two-dimensional Rosenbrock density, its current $n$-dimensional extensions, and their advantages and limitations. We then propose a new extension to arbitrary dimensions called the Hybrid Rosenbrock distribution, which is composed of conditional normal kernels arranged in such a way that preserves the key features of the original kernel. Moreover, due to its structure, the Hybrid Rosenbrock distribution is analytically tractable and possesses several desirable properties, which make it an excellent test model for computational algorithms.

Carrie J. McAdams
Jan 1, 1999; 19:431-441
Articles

Revolt RV 400 has finally been launched in India. With features like smartphone connectivity and multiple exhaust sounds, the RV 400 is definitely in a segment of its own. But what is an electric motorcycle to ride like? How does it compare with conventional motorcycles? Abhilasha Singh test rode the RV 400 to answer these […]

The Revolt RV400 has got the juice and the looks for city riding, but the looks are heavily inspired by a certain Chinese motorcycle

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The Toyota RAV4 EV is finally coming!

गुरूग्राम की कंपनी Revolt Intellicorp ने अपनी इलेक्ट्रिक बाइक RV400 की कीमतों में 5,000 रुपये की बढ़ोतरी की है. अब इस बाइक की नई कीमत 1.04 लाख रुपये हो गई है जो पहले 99,000 रुपये थी.

Tenda launches its latest F6 V4.0 Wi-Fi router today in the Indian market. The newly launched router is a successor to the existing series and the company claims it comes with better performance strengthening the existing portfolio of the brand. F6