Hasil untuk "Modern"

Qiang Guo, Yu-Xiang Wang, Dan Xu et al.

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease that primarily affects the lining of the synovial joints and is associated with progressive disability, premature death, and socioeconomic burdens. A better understanding of how the pathological mechanisms drive the deterioration of RA progress in individuals is urgently required in order to develop therapies that will effectively treat patients at each stage of the disease progress. Here we dissect the etiology and pathology at specific stages: (i) triggering, (ii) maturation, (iii) targeting, and (iv) fulminant stage, concomitant with hyperplastic synovium, cartilage damage, bone erosion, and systemic consequences. Modern pharmacologic therapies (including conventional, biological, and novel potential small molecule disease-modifying anti-rheumatic drugs) remain the mainstay of RA treatment and there has been significant progress toward achieving disease remission without joint deformity. Despite this, a significant proportion of RA patients do not effectively respond to the current therapies and thus new drugs are urgently required. This review discusses recent advances of our  understanding of RA pathogenesis, disease modifying drugs, and provides perspectives on next generation therapeutics for RA.Rheumatoid Arthritis: A preventable disease?The preclinical stages of rheumatoid arthritis (RA) represent a golden window for the development of therapies which could someday prevent the onset of clinical disease. The autoimmune processes underpinning RA usually begin many years before symptoms such as joint pain and stiffness emerge. Recent studies have identified some of the key cellular players driving these processes and begun to unpick how genetic and environmental risk factors combine to trigger them; they also suggest the existence of several distinct subtypes of RA, which require further exploration. Jiake Xu at the University of Western Australia in Perth and colleagues review current treatment strategies for RA and how such insights could ultimately lead to the earlier diagnosis of RA - as well as providing new opportunities for drug treatment and prevention through behavioral changes in high-risk individuals.

C. Gyawali, P. Kahrilas, E. Savarino et al.

Clinical history, questionnaire data and response to antisecretory therapy are insufficient to make a conclusive diagnosis of GERD in isolation, but are of value in determining need for further investigation. Conclusive evidence for reflux on oesophageal testing include advanced grade erosive oesophagitis (LA grades C and D), long-segment Barrett’s mucosa or peptic strictures on endoscopy or distal oesophageal acid exposure time (AET) >6% on ambulatory pH or pH-impedance monitoring. A normal endoscopy does not exclude GERD, but provides supportive evidence refuting GERD in conjunction with distal AET <4% and <40 reflux episodes on pH-impedance monitoring off proton pump inhibitors. Reflux-symptom association on ambulatory reflux monitoring provides supportive evidence for reflux triggered symptoms, and may predict a better treatment outcome when present. When endoscopy and pH or pH-impedance monitoring are inconclusive, adjunctive evidence from biopsy findings (histopathology scores, dilated intercellular spaces), motor evaluation (hypotensive lower oesophageal sphincter, hiatus hernia and oesophageal body hypomotility on high-resolution manometry) and novel impedance metrics (baseline impedance, postreflux swallow-induced peristaltic wave index) can add confidence for a GERD diagnosis; however, diagnosis cannot be based on these findings alone. An assessment of anatomy, motor function, reflux burden and symptomatic phenotype will therefore help direct management. Future GERD management strategies should focus on defining individual patient phenotypes based on the level of refluxate exposure, mechanism of reflux, efficacy of clearance, underlying anatomy of the oesophagogastric junction and psychometrics defining symptomatic presentations.

Kristian Kirsch

G. Ceballos, P. Ehrlich, A. Barnosky et al.

Humans are causing a massive animal extinction without precedent in 65 million years. The oft-repeated claim that Earth’s biota is entering a sixth “mass extinction” depends on clearly demonstrating that current extinction rates are far above the “background” rates prevailing between the five previous mass extinctions. Earlier estimates of extinction rates have been criticized for using assumptions that might overestimate the severity of the extinction crisis. We assess, using extremely conservative assumptions, whether human activities are causing a mass extinction. First, we use a recent estimate of a background rate of 2 mammal extinctions per 10,000 species per 100 years (that is, 2 E/MSY), which is twice as high as widely used previous estimates. We then compare this rate with the current rate of mammal and vertebrate extinctions. The latter is conservatively low because listing a species as extinct requires meeting stringent criteria. Even under our assumptions, which would tend to minimize evidence of an incipient mass extinction, the average rate of vertebrate species loss over the last century is up to 100 times higher than the background rate. Under the 2 E/MSY background rate, the number of species that have gone extinct in the last century would have taken, depending on the vertebrate taxon, between 800 and 10,000 years to disappear. These estimates reveal an exceptionally rapid loss of biodiversity over the last few centuries, indicating that a sixth mass extinction is already under way. Averting a dramatic decay of biodiversity and the subsequent loss of ecosystem services is still possible through intensified conservation efforts, but that window of opportunity is rapidly closing.

A. D'Amour, K. Heller, D. Moldovan et al.

ML models often exhibit unexpectedly poor behavior when they are deployed in real-world domains. We identify underspecification as a key reason for these failures. An ML pipeline is underspecified when it can return many predictors with equivalently strong held-out performance in the training domain. Underspecification is common in modern ML pipelines, such as those based on deep learning. Predictors returned by underspecified pipelines are often treated as equivalent based on their training domain performance, but we show here that such predictors can behave very differently in deployment domains. This ambiguity can lead to instability and poor model behavior in practice, and is a distinct failure mode from previously identified issues arising from structural mismatch between training and deployment domains. We show that this problem appears in a wide variety of practical ML pipelines, using examples from computer vision, medical imaging, natural language processing, clinical risk prediction based on electronic health records, and medical genomics. Our results show the need to explicitly account for underspecification in modeling pipelines that are intended for real-world deployment in any domain.

K. Różański, L. Araguás‐Araguás, R. Gonfiantini

Yanliang Liang, Yan-Hua Yao

S. Brunton, M. Budišić, E. Kaiser et al.

The field of dynamical systems is being transformed by the mathematical tools and algorithms emerging from modern computing and data science. First-principles derivations and asymptotic reductions are giving way to data-driven approaches that formulate models in operator theoretic or probabilistic frameworks. Koopman spectral theory has emerged as a dominant perspective over the past decade, in which nonlinear dynamics are represented in terms of an infinite-dimensional linear operator acting on the space of all possible measurement functions of the system. This linear representation of nonlinear dynamics has tremendous potential to enable the prediction, estimation, and control of nonlinear systems with standard textbook methods developed for linear systems. However, obtaining finite-dimensional coordinate systems and embeddings in which the dynamics appear approximately linear remains a central open challenge. The success of Koopman analysis is due primarily to three key factors: 1) there exists rigorous theory connecting it to classical geometric approaches for dynamical systems, 2) the approach is formulated in terms of measurements, making it ideal for leveraging big-data and machine learning techniques, and 3) simple, yet powerful numerical algorithms, such as the dynamic mode decomposition (DMD), have been developed and extended to reduce Koopman theory to practice in real-world applications. In this review, we provide an overview of modern Koopman operator theory, describing recent theoretical and algorithmic developments and highlighting these methods with a diverse range of applications. We also discuss key advances and challenges in the rapidly growing field of machine learning that are likely to drive future developments and significantly transform the theoretical landscape of dynamical systems.

R. Dorf, Naresh K. Sinha

U. Beck, A. Giddens, S. Lash

A. Giddens

Gerhard Weiss

J. García-Bellido

I review in these lectures our present understanding of the origin and evolution of the universe, making emphasis on the most recent observations of the acceleration of the universe, the precise measurements of the microwave background anisotropies, and the formation of structure like galaxies and clusters of galaxies from tiny primodial fluctuations generated during inflation.

R. Carroll

K. Ireland, M. Rosen

S. Kay

Paul R. Milgrom, John E. Roberts

R. Putnam, R. Leonardi, R. Nanetti

M. Minderer, J. Djolonga, Rob Romijnders et al.

Accurate estimation of predictive uncertainty (model calibration) is essential for the safe application of neural networks. Many instances of miscalibration in modern neural networks have been reported, suggesting a trend that newer, more accurate models produce poorly calibrated predictions. Here, we revisit this question for recent state-of-the-art image classification models. We systematically relate model calibration and accuracy, and find that the most recent models, notably those not using convolutions, are among the best calibrated. Trends observed in prior model generations, such as decay of calibration with distribution shift or model size, are less pronounced in recent architectures. We also show that model size and amount of pretraining do not fully explain these differences, suggesting that architecture is a major determinant of calibration properties.

Francesco Orabona

In this monograph, I introduce the basic concepts of Online Learning through a modern view of Online Convex Optimization. Here, online learning refers to the framework of regret minimization under worst-case assumptions. I present first-order and second-order algorithms for online learning with convex losses, in Euclidean and non-Euclidean settings. All the algorithms are clearly presented as instantiation of Online Mirror Descent or Follow-The-Regularized-Leader and their variants. Particular attention is given to the issue of tuning the parameters of the algorithms and learning in unbounded domains, through adaptive and parameter-free online learning algorithms. Non-convex losses are dealt through convex surrogate losses and through randomization. The bandit setting is also briefly discussed, touching on the problem of adversarial and stochastic multi-armed bandits. These notes do not require prior knowledge of convex analysis and all the required mathematical tools are rigorously explained. Moreover, all the proofs have been carefully chosen to be as simple and as short as possible.