Progress in the molecular biology of inherited bleeding disorders

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73391/1/j.1365-2516.2008.01718.x.pd

High fidelity progressive reinforcement learning for agile maneuvering UAVs

In this work, we present a high fidelity model based progressive reinforcement learning method for control system design for an agile maneuvering UAV. Our work relies on a simulation-based training and testing environment for doing software-in-the-loop (SIL), hardware-in-the-loop (HIL) and integrated flight testing within photo-realistic virtual reality (VR) environment. Through progressive learning with the high fidelity agent and environment models, the guidance and control policies build agile maneuvering based on fundamental control laws. First, we provide insight on development of high fidelity mathematical models using frequency domain system identification. These models are later used to design reinforcement learning based adaptive flight control laws allowing the vehicle to be controlled over a wide range of operating conditions covering model changes on operating conditions such as payload, voltage and damage to actuators and electronic speed controllers (ESCs). We later design outer flight guidance and control laws. Our current work and progress is summarized in this work

Hemophilia gene therapy knowledge and perceptions: Results of an international survey

Background Hemophilia gene therapy is a rapidly evolving therapeutic approach in which a number of programs are approaching clinical development completion. Objective The aim of this study was to evaluate knowledge and perceptions of a variety of health care practitioners and scientists about gene therapy for hemophilia. Methods This survey study was conducted February 1 to 18, 2019. Survey participants were members of the ISTH, European Hemophilia Consortium, European Hematology Association, or European Association for Hemophilia and Allied Disorders with valid email contacts. The online survey consisted of 36 questions covering demographic information, perceptions and knowledge of gene therapy for hemophilia, and educational preferences. Survey results were summarized using descriptive statistics. Results Of the 5117 survey recipients, 201 responded from 55 countries (4% response rate). Most respondents (66%) were physicians, and 59% were physicians directly involved in the care of people with hemophilia. Among physician respondents directly involved in hemophilia care, 35% lacked the ability to explain the science of adeno-associated viral gene therapy for hemophilia, and 40% indicated limited ability or lack of comfort answering patient questions about gene therapy for hemophilia based on clinical trial results to date. Overall, 75% of survey respondents answered 10 single-answer knowledge questions correctly, 13% incorrectly, and 12% were unsure of the correct answers. Conclusions This survey highlighted knowledge gaps and educational needs related to gene therapy for hemophilia and, along with other inputs, has informed the development of "Gene Therapy in Hemophilia: An ISTH Education Initiative.

Minimal dataset for post-registration surveillance of new drugs in hemophilia: communication from the SSC of the ISTH

Clinical epidemiolog

A Decision Support System to Predict Acute Fish Toxicity

We present a decision support system using a Bayesian network to predict acute fish toxicity from multiple lines of evidence. Fish embryo toxicity testing has been proposed as an alternative to using juvenile or adult fish in acute toxicity testing for hazard assessments of chemicals. The European Chemicals Agency has recommended the development of a so-called weight-of-evidence approach for strengthening the evidence from fish embryo toxicity testing. While weight-of-evidence approaches in the ecotoxicology and ecological risk assessment community in the past have been largely qualitative, we have developed a Bayesian network for using fish embryo toxicity data in a quantitative approach. The system enables users to efficiently predict the potential toxicity of a chemical substance based on multiple types of evidence including physical and chemical properties, quantitative structure-activity relationships, toxicity to algae and daphnids, and fish gill cytotoxicity. The system is demonstrated on three chemical substances of different levels of toxicity. It is considered as a promising step towards a probabilistic weight-of-evidence approach to predict acute fish toxicity from fish embryo toxicity.publishedVersio

Continuous-time spike-based reinforcement learning for working memory tasks

As the brain purportedly employs on-policy reinforcement learning compatible with SARSA learning, and most interesting cognitive tasks require some form of memory while taking place in continuous-time, recent work has developed plausible reinforcement learning schemes that are compatible with these requirements. Lacking is a formulation of both computation and learning in terms of spiking neurons. Such a formulation creates both a closer mapping to biology, and also expresses such learning in terms of asynchronous and sparse neural computation. We present a spiking neural network with memory that learns cognitive tasks in continuous time. Learning is biologically plausibly implemented using the AuGMeNT framework, and we show how separate spiking forward and feedback networks suffice for learning the tasks just as fast the analog CT-AuGMeNT counterpart, while computing efficiently using very few spikes: 1–20 Hz on average

Development of a hybrid Bayesian network model for predicting acute fish toxicity using multiple lines of evidence

A hybrid Bayesian network (BN) was developed for predicting the acute toxicity of chemicals to fish, using data from fish embryo toxicity (FET) testing in combination with other information. This model can support the use of FET data in a Weight-of-Evidence (WOE) approach for replacing the use of ju-venile fish. The BN predicted correct toxicity intervals for 69%–80% of the tested substances. The model was most sensitive to components quantified by toxicity data, and least sensitive to compo-nents quantified by expert knowledge. The model is publicly available through a web interface. Fur-ther development of this model should include additional lines of evidence, refinement of the discre-tisation, and training with a larger dataset for weighting of the lines of evidence. A refined version of this model can be a useful tool for predicting acute fish toxicity, and a contribution to more quantitative WOE approaches for ecotoxicology and environmental assessment more generally.publishedVersio

Mastering the game of Go without human knowledge

A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo