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Samuel A Cohen,1 Suzann Pershing1– 3 1Department of Ophthalmology, Stanford University School of Medicine, Stanford, CA, USA; 2VA Palo Alto Health Care System, Palo Alto, CA, USA; 3Byers Eye Institute at Stanford, Stanford, CA, USACorrespondence: Suzann Pershing, Email pershing@stanford.eduAim: To evaluate and quantify social media presence of ophthalmology peer-reviewed journals, professional societies, and eye health organizations, and to determine if there is a correlation between social media utilization and Twitter engagement metrics or journal impact measures.Methods: We searched for online profiles of 100 ophthalmology peer-reviewed journals, 333 professional ophthalmology societies, and 40 eye health organizations on Facebook, Twitter, and Instagram. Impact was quantified by recording the number of “likes” on Facebook and number of followers on Twitter and Instagram. We also used Twitonomy software to obtain advanced Twitter metrics for all journal accounts from 2018 to 2021, and compared to journal impact measured by SCImago Journal Rank (SJR) score, the h-index, and impact factor.Results: Eye health organizations averaged significantly greater Facebooks “likes” and Twitter followers than both peer-reviewed journals and professional societies (p < 0.0001). Of 100 journals studied, 30% were active on Twitter, 25% on Facebook, and 6% on Instagram. Slightly more than half of all journal-affiliated social media accounts were accessible from the journal website. Among journals with active Twitter accounts, total followers, total tweets, average retweets, and average “favorites” were all significantly positively correlated with academic metrics such as the SJR, h-index, and/or impact factor.Conclusion: Greater social media engagement is associated with higher ophthalmology journal impact metrics; however, ophthalmology journals and professional societies lag behind eye health organizations in social media engagement. Although unable to demonstrate causality, social media may be an underutilized visibility, communication, and dissemination tool.Keywords: social media, journal, ophthalmology, society, organization

Imitation learning (IL) seeks to teach agents specific tasks through expert demonstrations. One of the key approaches to IL is to define a distance between agent and expert and to find an agent policy that minimizes that distance. Optimal transport methods have been widely used in imitation learning as they provide ways to measure meaningful distances between agent and expert trajectories. However, the problem of how to optimally combine multiple expert demonstrations has not been widely studied. The standard method is to simply concatenate state (-action) trajectories, which is problematic when trajectories are multi-modal. We propose an alternative method that uses a multi-marginal optimal transport distance and enables the combination of multiple and diverse state-trajectories in the OT sense, providing a more sensible geometric average of the demonstrations. Our approach enables an agent to learn from several experts, and its efficiency is analyzed on OpenAI Gym control environments and demonstrates that the standard method is not always optimal.

We propose a novel framework for exploring weak and $L_2$ generalization errors of algorithms through the lens of differential calculus on the space of probability measures. Specifically, we consider the KL-regularized empirical risk minimization problem and establish generic conditions under which the generalization error convergence rate, when training on a sample of size $n$, is $\mathcal{O}(1/n)$. In the context of supervised learning with a one-hidden layer neural network in the mean-field regime, these conditions are reflected in suitable integrability and regularity assumptions on the loss and activation functions., 49 pages

We consider the problem of estimating the state of a continuous-time Markov chain from noisy observations. We show that the corresponding optimal filter is strictly contracting pathwise, when considered in the Hilbert projective space, and give explicit deterministic and pathwise rates of convergence. Using this, we provide alternative proofs of the robustness of optimal filters, improving on known error estimates, and derive rigorous and computable error bounds for approximate filters., 48 pages, 3 figures

We investigate whether the fee income from trades on the CFM is sufficient for the liquidity providers to hedge away the exposure to market risk. We first analyse this problem through the lens of continuous-time financial mathematics and derive an upper bound for not-arbitrage fee income that would make CFM efficient and liquidity provision fair. We then evaluate our findings by performing multi-agent simulations by varying CFM fees, market volatility, and rate of arrival of liquidity takers. We observe that, on average, fee income generated from liquidity provision is insufficient to compensate for market risk.

Numerically solving high-dimensional partial differential equations (PDEs) is a major challenge. Conventional methods, such as finite difference methods, are unable to solve high-dimensional PDEs due to the curse-of-dimensionality. A variety of deep learning methods have been recently developed to try and solve high-dimensional PDEs by approximating the solution using a neural network. In this paper, we prove global convergence for one of the commonly-used deep learning algorithms for solving PDEs, the Deep Galerkin Method (DGM). DGM trains a neural network approximator to solve the PDE using stochastic gradient descent. We prove that, as the number of hidden units in the single-layer network goes to infinity (i.e., in the ``wide network limit"), the trained neural network converges to the solution of an infinite-dimensional linear ordinary differential equation (ODE). The PDE residual of the limiting approximator converges to zero as the training time $\rightarrow \infty$. Under mild assumptions, this convergence also implies that the neural network approximator converges to the solution of the PDE. A closely related class of deep learning methods for PDEs is Physics Informed Neural Networks (PINNs). Using the same mathematical techniques, we can prove a similar global convergence result for the PINN neural network approximators. Both proofs require analyzing a kernel function in the limit ODE governing the evolution of the limit neural network approximator. A key technical challenge is that the kernel function, which is a composition of the PDE operator and the neural tangent kernel (NTK) operator, lacks a spectral gap, therefore requiring a careful analysis of its properties.

With the advent of large datasets, offline reinforcement learning (RL) is a promising framework for learning good decision-making policies without the need to interact with the real environment. However, offline RL requires the dataset to be reward-annotated, which presents practical challenges when reward engineering is difficult or when obtaining reward annotations is labor-intensive. In this paper, we introduce Optimal Transport Reward labeling (OTR), an algorithm that assigns rewards to offline trajectories, with a few high-quality demonstrations. OTR's key idea is to use optimal transport to compute an optimal alignment between an unlabeled trajectory in the dataset and an expert demonstration to obtain a similarity measure that can be interpreted as a reward, which can then be used by an offline RL algorithm to learn the policy. OTR is easy to implement and computationally efficient. On D4RL benchmarks, we show that OTR with a single demonstration can consistently match the performance of offline RL with ground-truth rewards., Comment: Published in ICLR 2023

Background and Aim: The COVID-19 pandemic was declared a national emergency in the United States in March 2020. The Centers for Medicare and Medicaid Services subsequently released recommendations that health-care facilities temporarily delay elective surgeries and non-essential medical procedures. Disruptions to medical care significantly impacted cancer patients, with cancer screenings halted and nonurgent cancer surgeries postponed as health-care facilities shifted resources toward the COVID-19 pandemic. Although it has been reported that cancer screening rates decreased dramatically in the United States in 2020, it is unclear whether this trend was driven by factors related to public interest in cancer and/or cancer screening as opposed to other factors such as clinical backlogs, pandemic-related policies, and/or resource limitations. The purpose of this study was to use the Google Trends tool to evaluate public interest in six common malignancies and four common cancer screening methods during the COVID-19 pandemic. Methods: We used the Google Trends tool to quantify public interest in six different malignancies (Breast Cancer, Colon Cancer, Lung Cancer, Prostate Cancer, Thyroid Cancer, and Cervical Cancer) and four cancer screening methods (Pap Smear, Lung Cancer Screening, Mammogram, and Colonoscopy) in the United States during the COVID-19 pandemic. Welch’s t-tests were used to compare monthly search volumes during the COVID-19 pandemic (2020) to the 4 years before the pandemic (2016 – 2019) for all ten search terms included in our study. We used Benjamini-Hochberg to adjust raw p values to account for multiple statistical comparisons. The level of statistical significance was defined by choosing a false discovery rate of 0.05. Results: Our results indicate significantly reduced interest in all malignancies studied at the beginning of the COVID-19 pandemic. Public interest in [‘Breast Cancer’], [‘Colon Cancer’], [‘Lung Cancer’], [‘Thyroid Cancer’], and [‘Cervical Cancer’] significantly decreased in the months of March, April, May, and June 2020 when compared with public interest in 2016-2019. Public interest in cancer screening methods such as [‘Pap Smear’], [‘Lung Cancer Screening’], [‘Mammogram’], and [‘Colonoscopy’] significantly deceased in the months of April and May compared to 2016 – 2019 values. However, decreased public interest in cancer screening methods was temporary, with Google search volumes returning to pre-pandemic levels in June 2020 – December 2020. Conclusion: There was significantly reduced public interest in both common malignancies and cancer screening methods at the beginning of the COVID-19 pandemic in the United States. However, after an initial decline, public interest as indicated by Google search volumes quickly returned to pre-pandemic levels in the second half of the calendar year 2020. In addition, trends in public interest in cancer screening as indicated by Google search volumes aligned with cancer screening uptake rates in the United States during the study period. This finding suggests that Google Trends may serve as an effective tool in gauging the public’s interest in cancer and/or cancer screenings in the United States, which makes it a valuable resource that can be used to inform decisions aimed at improving cancer screening rates in the future. Relevance for Patients: The Google Trends tool can be used to measure public interest in various malignancies and their associated screening methods. Google Trends data may be used to inform measures aimed at improving cancer screening uptake.

Synthetic data has gained significant momentum thanks to sophisticated machine learning tools that enable the synthesis of high-dimensional datasets. However, many generation techniques do not give the data controller control over what statistical patterns are captured, leading to concerns over privacy protection. While synthetic records are not linked to a particular real-world individual, they can reveal information about users indirectly which may be unacceptable for data owners. There is thus a need to empirically verify the privacy of synthetic data -- a particularly challenging task in high-dimensional data. In this paper we present a general framework for synthetic data generation that gives data controllers full control over which statistical properties the synthetic data ought to preserve, what exact information loss is acceptable, and how to quantify it. The benefits of the approach are that (1) one can generate synthetic data that results in high utility for a given task, while (2) empirically validating that only statistics considered safe by the data curator are used to generate the data. We thus show the potential for synthetic data to be an effective means of releasing confidential data safely, while retaining useful information for analysts.

We study the use of amortized optimization to predict optimal transport (OT) maps from the input measures, which we call Meta OT. This helps repeatedly solve similar OT problems between different measures by leveraging the knowledge and information present from past problems to rapidly predict and solve new problems. Otherwise, standard methods ignore the knowledge of the past solutions and suboptimally re-solve each problem from scratch. We instantiate Meta OT models in discrete and continuous settings between grayscale images, spherical data, classification labels, and color palettes and use them to improve the computational time of standard OT solvers. Our source code is available at http://github.com/facebookresearch/meta-ot, ICML 2023

Solving high-dimensional partial differential equations (PDEs) is a major challenge in scientific computing. We develop a new numerical method for solving elliptic-type PDEs by adapting the Q-learning algorithm in reinforcement learning. Our "Q-PDE" algorithm is mesh-free and therefore has the potential to overcome the curse of dimensionality. Using a neural tangent kernel (NTK) approach, we prove that the neural network approximator for the PDE solution, trained with the Q-PDE algorithm, converges to the trajectory of an infinite-dimensional ordinary differential equation (ODE) as the number of hidden units $\rightarrow \infty$. For monotone PDE (i.e. those given by monotone operators, which may be nonlinear), despite the lack of a spectral gap in the NTK, we then prove that the limit neural network, which satisfies the infinite-dimensional ODE, converges in $L^2$ to the PDE solution as the training time $\rightarrow \infty$. More generally, we can prove that any fixed point of the wide-network limit for the Q-PDE algorithm is a solution of the PDE (not necessarily under the monotone condition). The numerical performance of the Q-PDE algorithm is studied for several elliptic PDEs.

Continuous Normalizing Flows (CNFs) are a class of generative models that transform a prior distribution to a model distribution by solving an ordinary differential equation (ODE). We propose to train CNFs on manifolds by minimizing probability path divergence (PPD), a novel family of divergences between the probability density path generated by the CNF and a target probability density path. PPD is formulated using a logarithmic mass conservation formula which is a linear first order partial differential equation relating the log target probabilities and the CNF's defining vector field. PPD has several key benefits over existing methods: it sidesteps the need to solve an ODE per iteration, readily applies to manifold data, scales to high dimensions, and is compatible with a large family of target paths interpolating pure noise and data in finite time. Theoretically, PPD is shown to bound classical probability divergences. Empirically, we show that CNFs learned by minimizing PPD achieve state-of-the-art results in likelihoods and sample quality on existing low-dimensional manifold benchmarks, and is the first example of a generative model to scale to moderately high dimensional manifolds., Comment: ICML 2022

IAEC/SNRC (Israel) is constructing an accelerator fa-cility, SARAF, for neutron production. It is based on a linac accelerating 5 mA CW deuteron and proton beam up to 40 MeV. As a first phase, IAEC constructed and operated a linac (SARAF Phase I), from which remains an ECR ion source, a Low-Energy Beam Transport (LEBT) line and a 4-rod RFQ. Since 2015, IAEC and CEA (France) are collaborating in the second phase, consisting in manufacturing of the linac (Figure 1). The injector control-system has been recently updated and the Medium Energy Beam Transport (MEBT) line has been installed and integrated to the infrastructure. It has been partially commissioned during the first semester of 2022. This paper presents the results of the integration, tests and commissioning of the injector and MEBT, be-fore delivery of the cryomodules., Proceedings of the 31st International Linear Accelerator Conference, LINAC2022, Liverpool, UK

This explainer document aims to provide an overview of the current state of the rapidly expanding work on synthetic data technologies, with a particular focus on privacy. The article is intended for a non-technical audience, though some formal definitions have been given to provide clarity to specialists. This article is intended to enable the reader to quickly become familiar with the notion of synthetic data, as well as understand some of the subtle intricacies that come with it. We do believe that synthetic data is a very useful tool, and our hope is that this report highlights that, while drawing attention to nuances that can easily be overlooked in its deployment., Comment: Commissioned by the Royal Society. 57 pages 2 figures

BACKGROUND: The shift to virtual interviews during the COVID-19 pandemic has elevated the vital role of Accreditation Council for Graduate Medical Education residency program websites in conveying information to applicants. The purpose of our study was to assess the recruitment, education, and diversity and inclusion content on websites for anesthesiology residency programs. Second, we aimed to test the hypothesis that the content scores of websites are higher in programs with more National Institutes of Health funding, in programs that are university-based versus community-based, and in larger programs, as measured by number of residents. METHODS: Two independent reviewers evaluated the websites of the 159 anesthesiology residency programs accredited by the Accreditation Council for Graduate Medical Education for the presence (yes/no) of 12 recruitment, 6 education, and 8 diversity and inclusion criteria. Multiple linear regression was used to determine which program factors were most associated with total website content score. RESULTS: Anesthesiology residency program websites contained a mean of 12.9 (SD = 3.4; range, 3–21) of the 26 study-defined criteria. The most common recruitment, education, and diversity and inclusion criteria were, respectively, program description, rotation information, and community demographics. Controlling for program factors, a university-based affiliation (P = .016) was associated with higher website content scores. CONCLUSIONS: There is large variation in the recruitment, education, and diversity and inclusion content on anesthesiology residency program websites nationally. Since program websites averaged only half of criteria, this may provide an impetus for programs to modify their websites, which may inform applicant decisions about which programs align with their training and career goals.

Pulse pile-up in pulse-height energy analyzers increases when the incident rate of pulses increases relative to the inverse of the dead time per pulse of the detection system. Changes in the observed energy distributions with incident rate and detector-electronics-formed pulse shape then occur. We focus on weak high energy tails in X-ray spectra, important for measurements on partially ionized, warm, pure-hydrogen plasma. A first-principles two-photon pulse-pile-up model is derived specific to trapezoidal-shaped pulses; quantitative agreement is found between the measurements and the model predictions. The modeling is then used to diagnose pulse-pile-up tail artifacts and mitigate them in relatively low count-rate spectra., Comment: 8 pages, 12 figures, preprint for Plasmas MDPI journal

Modeling distributions on Riemannian manifolds is a crucial component in understanding non-Euclidean data that arises, e.g., in physics and geology. The budding approaches in this space are limited by representational and computational tradeoffs. We propose and study a class of flows that uses convex potentials from Riemannian optimal transport. These are universal and can model distributions on any compact Riemannian manifold without requiring domain knowledge of the manifold to be integrated into the architecture. We demonstrate that these flows can model standard distributions on spheres, and tori, on synthetic and geological data. Our source code is freely available online at http://github.com/facebookresearch/rcpm, ICML 2021

Transient oligomeric species formed during the aggregation process of the 42-residue form of the amyloid-β peptide (Aβ42) are key pathogenic agents in Alzheimer’s disease (AD). To investigate the relationship between Aβ42 aggregation and its cytotoxicity and the influence of a potential drug on both phenomena, we have studied the effects of trodusquemine. This aminosterol enhances the rate of aggregation by promoting monomer-dependent secondary nucleation, but significantly reduces the toxicity of the resulting oligomers to neuroblastoma cells by inhibiting their binding to the cellular membranes. When administered to a C. elegans model of AD, we again observe an increase in aggregate formation alongside the suppression of Aβ42-induced toxicity. In addition to oligomer displacement, the reduced toxicity could also point towards an increased rate of conversion of oligomers to less toxic fibrils. The ability of a small molecule to reduce the toxicity of oligomeric species represents a potential therapeutic strategy against AD., Transient oligomeric species of the amyloid-β peptide (Aβ42) have been identified as key pathogenic agents in Alzheimer’s disease. Here the authors find that the aminosterol trodusquemine enhances Aβ42 aggregation and suppresses Aβ42-induced toxicity by displacing oligomers from cell membranes.

Multi-marginal optimal transport enables one to compare multiple probability measures, which increasingly finds application in multi-task learning problems. One practical limitation of multi-marginal transport is computational scalability in the number of measures, samples and dimensionality. In this work, we propose a multi-marginal optimal transport paradigm based on random one-dimensional projections, whose (generalized) distance we term the sliced multi-marginal Wasserstein distance. To construct this distance, we introduce a characterization of the one-dimensional multi-marginal Kantorovich problem and use it to highlight a number of properties of the sliced multi-marginal Wasserstein distance. In particular, we show that (i) the sliced multi-marginal Wasserstein distance is a (generalized) metric that induces the same topology as the standard Wasserstein distance, (ii) it admits a dimension-free sample complexity, (iii) it is tightly connected with the problem of barycentric averaging under the sliced-Wasserstein metric. We conclude by illustrating the sliced multi-marginal Wasserstein on multi-task density estimation and multi-dynamics reinforcement learning problems.

Gaussian processes (GPs) are nonparametric Bayesian models that have been applied to regression and classification problems. One of the approaches to alleviate their cubic training cost is the use of local GP experts trained on subsets of the data. In particular, product-of-expert models combine the predictive distributions of local experts through a tractable product operation. While these expert models allow for massively distributed computation, their predictions typically suffer from erratic behaviour of the mean or uncalibrated uncertainty quantification. By calibrating predictions via a tempered softmax weighting, we provide a solution to these problems for multiple product-of-expert models, including the generalised product of experts and the robust Bayesian committee machine. Furthermore, we leverage the optimal transport literature and propose a new product-of-expert model that combines predictions of local experts by computing their Wasserstein barycenter, which can be applied to both regression and classification., ICML 2020

The Asymptotic Randomised Control (ARC) algorithm provides a rigorous approximation to the optimal strategy for a wide class of Bayesian bandits, while retaining low computational complexity. In particular, the ARC approach provides nearly optimal choices even when the payoffs are correlated or more than the reward is observed. The algorithm is guaranteed to asymptotically optimise the expected discounted payoff, with error depending on the initial uncertainty of the bandit. In this paper, we extend the ARC framework to consider a batched bandit problem where observations arrive from a generalised linear model. In particular, we develop a large sample approximation to allow correlated and generally distributed observation. We apply this to a classic dynamic pricing problem based on a Bayesian hierarchical model and demonstrate that the ARC algorithm outperforms alternative approaches.

Cross-domain imitation learning studies how to leverage expert demonstrations of one agent to train an imitation agent with a different embodiment or morphology. Comparing trajectories and stationary distributions between the expert and imitation agents is challenging because they live on different systems that may not even have the same dimensionality. We propose Gromov-Wasserstein Imitation Learning (GWIL), a method for cross-domain imitation that uses the Gromov-Wasserstein distance to align and compare states between the different spaces of the agents. Our theory formally characterizes the scenarios where GWIL preserves optimality, revealing its possibilities and limitations. We demonstrate the effectiveness of GWIL in non-trivial continuous control domains ranging from simple rigid transformation of the expert domain to arbitrary transformation of the state-action space., Comment: ICLR 2022

Supplemental Material, sj-docx-1-tpx-10.1177_01926233211027474 for Chimeric Mouse With Humanized Liver Is an Appropriate Animal Model to Investigate Mode of Action for Porphyria-Mediated Hepatocytotoxicity by Ayumi Eguchi, Satoki Fukunaga, Keiko Ogata, Masahiko Kushida, Hiroyuki Asano, Samuel M. Cohen and Tokuo Sukata in Toxicologic Pathology

We consider a general multi-armed bandit problem with correlated (and simple contextual and restless) elements, as a relaxed control problem. By introducing an entropy regularisation, we obtain a smooth asymptotic approximation to the value function. This yields a novel semi-index approximation of the optimal decision process. This semi-index can be interpreted as explicitly balancing an exploration-exploitation trade-off as in the optimistic (UCB) principle where the learning premium explicitly describes asymmetry of information available in the environment and non-linearity in the reward function. Performance of the resulting Asymptotic Randomised Control (ARC) algorithm compares favourably well with other approaches to correlated multi-armed bandits.

Barycentric averaging is a principled way of summarizing populations of measures. Existing algorithms for estimating barycenters typically parametrize them as weighted sums of Diracs and optimize their weights and/or locations. However, these approaches do not scale to high-dimensional settings due to the curse of dimensionality. In this paper, we propose a scalable and general algorithm for estimating barycenters of measures in high dimensions. The key idea is to turn the optimization over measures into an optimization over generative models, introducing inductive biases that allow the method to scale while still accurately estimating barycenters. We prove local convergence under mild assumptions on the discrepancy showing that the approach is well-posed. We demonstrate that our method is fast, achieves good performance on low-dimensional problems, and scales to high-dimensional settings. In particular, our approach is the first to be used to estimate barycenters in thousands of dimensions., In submission

Pyroxasulfone induced a low incidence of urinary bladder tumors in male rats in a 2-year bioassay at 1000 and 2000 ppm, with occasional urinary calculi. No increased incidence of tumors of any tissue occurred in female rats or in mice of either gender. We performed three short-term studies to evaluate early development of pyroxasulfone-induced urinary crystals and urothelial cytotoxicity with consequent regenerative proliferation. First, male rats were treated with dietary 50, 1000 or 2000 ppm pyroxasulfone for 1, 3 or 7 days. The urothelium was examined by light and scanning electron microscopy (LM, SEM) and bromodeoxyuridine labeling index (BrdU LI). In two other studies, male rats were treated with dietary 20 000 ppm pyroxasulfone for 1 week. Urine collected at various times of day was examined by SEM and energy dispersive spectroscopy (EDS) or by LM, SEM, EDS, and infrared spectroscopy (IFS). Urinary crystals were present at various time points. EDS and IFS showed some contained calcium; others contained organic matter. Cytotoxicity was detected by SEM as cellular swelling, craters, and necrosis and by LM as cellular hypertrophy. Increased cell proliferation was detected by LM (hyperplasia), SEM (piling up of round cells), and by increased BrdU LI. There was no evidence of increased apoptosis. These findings support a mode of action for pyroxasulfone-associated bladder tumors in male rats involving formation of urinary crystals leading to urothelial cytotoxicity and regenerative proliferation. This is a high dose phenomenon, therefore, pyroxasulfone is not likely to be carcinogenic to humans at exposure levels that do not cause crystals with subsequent calculi formation in the urinary tract.

A general reaction network for filamentous self-assembly unifies mechanistic descriptions and links the overall scaling behaviour to the underlying rate-determining steps., The formation of filaments from naturally occurring protein molecules is a process at the core of a range of functional and aberrant biological phenomena, such as the assembly of the cytoskeleton or the appearance of aggregates in Alzheimer's disease. The macroscopic behaviour associated with such processes is remarkably diverse, ranging from simple nucleated growth to highly cooperative processes with a well-defined lagtime. Thus, conventionally, different molecular mechanisms have been used to explain the self-assembly of different proteins. Here we show that this range of behaviour can be quantitatively captured by a single unifying Petri net that describes filamentous growth in terms of aggregate number and aggregate mass concentrations. By considering general features associated with a particular network connectivity, we are able to establish directly the rate-determining steps of the overall aggregation reaction from the system's scaling behaviour. We illustrate the power of this framework on a range of different experimental and simulated aggregating systems. The approach is general and will be applicable to any future extensions of the reaction network of filamentous self-assembly.

The aggregates of the Aβ peptide associated with Alzheimer's disease are able to both grow in size as well as generate, through secondary nucleation, new small oligomeric species, that are major cytotoxins associated with neuronal death. Despite the importance of these amyloid fibril-dependent processes, their structural and molecular underpinnings have remained challenging to elucidate. Here, we consider two molecular chaperones: the Brichos domain, which suppresses specifically secondary nucleation processes, and clusterin which our results show is capable of inhibiting, specifically, the elongation of Aβ fibrils at remarkably low substoichiometric ratios. Microfluidic diffusional sizing measurements demonstrate that this inhibition originates from interactions of clusterin with fibril ends with high affinity. Kinetic experiments in the presence of both molecular chaperones reveal that their inhibitory effects are additive and noncooperative, thereby indicating that the reactive sites associated with the formation of new aggregates and the growth of existing aggregates are distinct.

Transient oligomeric species formed during the aggregation process of the 42-residue form of the amyloid-β peptide (Aβ 42 ) are key pathogenic agents in Alzheimer’s disease (AD). To investigate the relationship between Aβ 42 aggregation and its cytotoxicity and the influence of a potential drug on both phenomena, we have studied the effects of trodusquemine. This aminosterol enhances the rate of aggregation by promoting monomer-dependent secondary nucleation, but significantly reduces the toxicity of the resulting oligomers to neuroblastoma cells by inhibiting their binding to the cellular membranes. When administered to a C. elegans model of AD, we again observe an increase in aggregate formation alongside the suppression of Aβ 42 -induced toxicity. In addition to oligomer displacement, the reduced toxicity could also point towards an increased rate of conversion of oligomers to less toxic fibrils. The ability of a small molecule to reduce the toxicity of oligomeric species represents a potential therapeutic strategy against AD.

In this case report, a method to achieve an adequate compensating curve and bilateral balanced lingualized occlusion in a case requiring maxillary complete denture and mandibular Kennedy Class II removable partial and fixed prosthodontic rehabilitation is described.

Screening for carcinogens in general, and for the urinary bladder specifically, traditionally involves a two-year bioassay in rodents, the results of which often do not have direct relevance to humans with respect to mode of action (MOA) and/or dose response. My proposal describes a multi-step short-term (90 day) screening process that characterizes known human urinary bladder carcinogens, and identifies those reported in rodent two-year bioassays. The initial step is screening for urothelial proliferation, by microscopy or by increased Ki-67 labeling index. If these are negative, the agent is not a urinary bladder carcinogen. If either of these is positive, an MOA and dose response analysis are performed. DNA reactivity is evaluated. If the chemical is non-DNA reactive, evaluation for cytotoxicity is performed. This involves examination of the urothelium and urine, the latter to identify the generation of urinary solids (

We consider stochastic volatility models under parameter uncertainty and investigate how model derived prices of European options are affected. We let the pricing parameters evolve dynamically in time within a specified region, and formalise the problem as a control problem where the control acts on the parameters to maximise/minimise the option value. Through a dual representation with backward stochastic differential equations, we obtain explicit equations for Heston's model and investigate several numerical solutions thereof. In an empirical study, we apply our results to market data from the S&P 500 index where the model is estimated to historical asset prices. We find that the conservative model-prices cover 98% of the considered market-prices for a set of European call options.

Background and aimsUse of GI endoscopy is historically lower in nonwhite ethnic and racial groups compared with whites. These disparities are multifactorial but likely contribute to differences in GI clinical outcomes. We sought to improve endoscopy uptake overall and in minorities by predictive overbooking and active recruitment in a hospital-based GI clinic.MethodsFrom January to October 2014, we alternated between traditional booking for Veterans Affairs Healthcare Network patients with a physician recommendation for endoscopy and active recruitment of patients to fill projected open endoscopy appointment slots. On intervention weeks, patients attending a GI clinic were given the opportunity to "fast track" to an endoscopy appointment on short notice. During control weeks, patients were not actively recruited. We compared uptake of endoscopy appointments in both groups and performed logistic regression to determine predictors of participation in fast-track active recruitment.ResultsDuring fast-track active recruitment for endoscopy, the clinic recruited an additional 111 patients for endoscopy over passive recruitment, including 46 African Americans (41.4%). In a logistic regression model controlling for demographic and clinical characteristics, African Americans were twice as likely (adjusted OR, 1.99; 95% CI, 1.26-3.17) than whites to participate in the fast-track option for recommended endoscopy.ConclusionsInterventions to actively recruit patients for endoscopy increased the overall percentage of GI clinic patients undergoing endoscopy and disproportionately improved endoscopy appointment uptake in African Americans.

La maladie cœliaque (ou intolérance au gluten) est une entéropathie dont l'incidence n'a cessé d'augmenter durant les dernières décennies. Le nombre d'études médicales récentes témoigne de l'intérêt grandissant pour cette pathologie aux multiples aspects cliniques et aux complications pouvant engager le pronostic vital. Malgré des outils diagnostiques de plus en plus performants, la maladie cœliaque reste encore sous-diagnostiquée dans ses formes atypiques et silencieuses. Cependant, un examen bucco-dentaire à la recherche des manifestations buccales par un chirurgien-dentiste informé peut constituer l'une des premières étapes dans le diagnostic et donc la prise en charge de cette maladie.

Projeto de Graduação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Licenciado em Fisioterapia Introdução: Espondilite anquilosante (EA) é uma doença reumática crónica sistémica, sendo um protótipo de espondiloartropatias seronegativas caracterizada por inflamação, afetando predominantemente a coluna vertebral, os métodos de tratamento recomendados são fisioterapia supervisionada recorrendo-se ao tratamento convencional ou à prática de hidroterapia. Objetivo: Determinar os efeitos da hidroterapia na dor e capacidade funcional, nos pacientes com espondilite anquilosante (EA). Metodologia: Pesquisa computorizada nas bases de dados PEDro, Science Direct, EBSCO e PubMed/Medline, para identificar os estudos randomizados controlados que avaliaram os efeitos da hidroterapia na EA. Resultados: Nesta revisão foram incluídos 5 estudos envolvendo 251 indivíduos, com classificação 5,6 metodológica na escala de PEDro. Conclusões: A hidroterapia parece influenciar positivamente no tratamento de pacientes com espondilite anquilosante melhorando parâmetros como a dor e a capacidade funcional. No entanto estes efeitos parecem perder-se com o tempo, e por isso as diferenças entre a hidroterapia e outro tipo de tratamentos parecem esbater-se com o tempo. Introduction: Ankylosing spondylitis (AS) is a chronic systemic rheumatic disease, being a prototype of seronegative spondyloarthropathies characterized by inflammation, affecting predominantly the vertebral column, the recommended treatment methods are supervised physiotherapy using the conventional exercices or the practice of hydrotherapy. Objective: To determine the effects of hydrotherapy on pain and functional capacity in patients with ankylosing spondylitis (AS). Methods: Computerized research in the databases PEDro, Science Direct, EBSCO and PubMed / Medline, to identify randomized controlled trials that evaluated the effects of hydrotherapy in AS. Results This review included 5 studies involving 251 individuals, with a methodological classification of 5.6 on the PEDro scale. Conclusions: Hydrotherapy has shown its effectiveness on pain, stiffness and mobility of the spine, which have a direct impact on patients' functional capacity. N/A

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Conventional approaches for simulating steady-state distributions of particles under diffusive and advective transport at high P\'eclet numbers involve solving the diffusion and advection equations in at least two dimensions. Here, we present an alternative computational strategy by combining a particle-based rather than a field-based approach with the initialisation of particles in proportion to their flux. This method allows accurate prediction of the steady state and is applicable even at high P\'eclet numbers where traditional particle-based Monte-Carlo methods starting from randomly initialised particle distributions fail. We demonstrate that generating a flux of particles according to a predetermined density and velocity distribution at a single fixed time and initial location allows for accurate simulation of mass transport under flow. Specifically, upon initialisation in proportion to their flux, these particles are propagated individually and detected by summing up their Monte-Carlo trajectories in predefined detection regions. We demonstrate quantitative agreement of the predicted concentration profiles with the results of experiments performed with fluorescent particles in microfluidic channels under continuous flow. This approach is computationally advantageous and readily allows non-trivial initial distributions to be considered. In particular, this method is highly suitable for simulating advective and diffusive transport in microfluidic devices.

Conventional approaches for simulating steady-state distributions of particles under diffusive and advective transport at high P��clet numbers involve solving the diffusion and advection equations in at least two dimensions. Here, we present an alternative computational strategy by combining a particle-based rather than a field-based approach with the initialisation of particles in proportion to their flux. This method allows accurate prediction of the steady state and is applicable even at high P��clet numbers where traditional particle-based Monte-Carlo methods starting from randomly initialised particle distributions fail. We demonstrate that generating a flux of particles according to a predetermined density and velocity distribution at a single fixed time and initial location allows for accurate simulation of mass transport under flow. Specifically, upon initialisation in proportion to their flux, these particles are propagated individually and detected by summing up their Monte-Carlo trajectories in predefined detection regions. We demonstrate quantitative agreement of the predicted concentration profiles with the results of experiments performed with fluorescent particles in microfluidic channels under continuous flow. This approach is computationally advantageous and readily allows non-trivial initial distributions to be considered. In particular, this method is highly suitable for simulating advective and diffusive transport in microfluidic devices.

In this paper we look at ergodic BSDEs in the case where the forward dynamics are given by the solution to a non-autonomous (time-periodic coefficients) Ornstein-Uhlenbeck SDE with L\'evy noise, taking values in a separable Hilbert space. We establish the existence of a unique bounded solution to an infinite horizon discounted BSDE. We then use the vanishing discount approach, together with coupling techniques, to obtain a Markovian solution to the EBSDE. We also prove uniqueness under certain growth conditions. Applications are then given, in particular to risk-averse ergodic optimal control and power plant evaluation under uncertainty.

We consider a self-exciting counting process, the parameters of which depend on a hidden finite-state Markov chain. We derive the optimal filter and smoother for the hidden chain based on observation of the jump process. This filter is in closed form and is finite dimensional. We demonstrate the performance of this filter both with simulated data, and by analysing the `flash crash' of 6th May 2010 in this framework.

In this paper, we study reflected backward stochastic difference equations (RBSDEs for short) with finitely many states in discrete time. The general existence and uniqueness result, as well as comparison theorems for the solutions, are established under mild assumptions. The connections between RBSDEs and optimal stopping problems are also given. Then we apply the obtained results to explore optimal stopping problems under $g$-expectation. Finally, we study the pricing of American contingent claims in our context., Comment: 29 pages

100mg/kgのcyclophosphamideを腹腔内投与することにより, 出血性膀胱炎を誘発し, chitosan (0.3ml)を膀胱内注入し, 検討した結果, cyclophosphamide投与1時間後の投与群において死亡率の減少と, 肉眼的膀胱出血, 壊死および炎症の発生率の低下が認められた。膀胱炎発生後のchitosan膀胱内注入は, 特に繰り返し施行した場合, cyclophosphamideによる変化を悪化させる傾向がみられた。cyclophosphamide投与1時間後の投与群では, chitosanは出血性膀胱炎の発生を抑制する可能性が示唆された。さらに, cyclophosphamideによる腎の変化も認められた, Hemorrhagic cystitis is a common problem following cyclophosphamide or radiation therapy. Chitosan has been shown to be an effective hemostatic agent and promoter of wound healing in animal experiments. We evaluated the safety and efficacy of intravesical chitosan in an animal model of cyclophosphamide cystitis. Hemorrhagic cystitis was induced in female F344 rats by intraperitoneal cyclophosphamide, 100 mg/kg. Chitosan solution (0.3 ml) was instilled intravesically on day 1 (Group 1), on days 1, 3, and 5 (Group 2), or 1 hour after the administration of cyclophosphamide (Group 3). The rats in group 4 were treated with chitosan diluent on day 1 after cyclophosphamide, and the rats in group 5 received intravesical chitosan without cyclophosphamide. Sequential examination revealed decreased mortality and lower incidences of severe bladder bleeding, necrosis and inflammation in Group 3. Treatment delayed until after the appearance of the cystitis, especially repeated treatments, appeared to make the cyclophosphamide-induced changes worse. Used within 1 hour of cyclophosphamide administration, before the cystitis develops, chitosan seemed to have the possibility to inhibit the appearance of hemorrhagic cystitis. In addition to the changes in the bladder, severe changes occurred in the kidneys secondary to cyclophosphamide.