Automated Active Learning with a Robot

In the field of automated processes in industry, a major goal is for robots to solve new tasks without costly adaptions. Therefore, it is of advantage if the robot can perform new tasks independently while the learning process is intuitively understandable for humans. In this article, we present a highly automated and intuitive active learning algorithm for robots. It learns new classification tasks by asking questions to a human teacher and automatically decides when to stop the learning process by self-assessing its confidence. This so-called stopping criterion is required to guarantee a fully automated procedure. Our approach is highly interactive as we use speech for communication and a graphical visualization tool. The latter provides information about the learning progress and the stopping criterion, which helps the human teacher in understanding the training process better. The applicability of our approach is shown and evaluated on a real Baxter robot

A Stopping Criterion for Transductive Active Learning

In transductive active learning, the goal is to determine the correct labels for an unlabeled, known dataset. Therefore, we can either ask an oracle to provide the right label at some cost or use the prediction of a classifier which we train on the labels acquired so far. In contrast, the commonly used (inductive) active learning aims to select instances for labeling out of the unlabeled set to create a generalized classifier, which will be deployed on unknown data. This article formally defines the transductive setting and shows that it requires new solutions. Additionally, we formalize the theoretically cost-optimal stopping point for the transductive scenario. Building upon the probabilistic active learning framework, we propose a new transductive selection strategy that includes a stopping criterion and show its superiority

Stream-based active learning for sliding windows under the influence of verification latency

Stream-based active learning (AL) strategies minimize the labeling effort by querying labels that improve the classifier’s performance the most. So far, these strategies neglect the fact that an oracle or expert requires time to provide a queried label. We show that existing AL methods deteriorate or even fail under the influence of such verification latency. The problem with these methods is that they estimate a label’s utility on the currently available labeled data. However, when this label would arrive, some of the current data may have gotten outdated and new labels have arrived. In this article, we propose to simulate the available data at the time when the label would arrive. Therefore, our method Forgetting and Simulating (FS) forgets outdated information and simulates the delayed labels to get more realistic utility estimates. We assume to know the label’s arrival date a priori and the classifier’s training data to be bounded by a sliding window. Our extensive experiments show that FS improves stream-based AL strategies in settings with both, constant and variable verification latency

A flavin-dependent halogenase from metagenomic analysis prefers bromination over chlorination

Neubauer P, Widmann C, Wibberg D, et al. A flavin-dependent halogenase from metagenomic analysis prefers bromination over chlorination. PLoS ONE. 2018;13(5): e0196797.Flavin-dependent halogenases catalyse halogenation of aromatic compounds. In most cases, this reaction proceeds with high regioselectivity and requires only the presence of FADH2, oxygen, and halide salts. Since marine habitats contain high concentrations of halides, organisms populating the oceans might be valuable sources of yet undiscovered halogenases. A new Hidden-Markov-Model (HMM) based on the PFAM tryptophan halogenase model was used for the analysis of marine metagenomes. Eleven metagenomes were screened leading to the identification of 254 complete or partial putative flavin-dependent halogenase genes. One predicted halogenase gene (brvH) was selected, codon optimised for E. coli, and overexpressed. Substrate screening revealed that this enzyme represents an active flavin-dependent halogenase able to convert indole to 3-bromoindole. Remarkably, bromination prevails also in a large excess of chloride. The BrvH crystal structure is very similar to that of tryptophan halogenases but reveals a substrate binding site that is open to the solvent instead of being covered by a loop

Designing a patient-centered personal health record to promote preventive care

<p>Abstract</p> <p>Background</p> <p>Evidence-based preventive services offer profound health benefits, yet Americans receive only half of indicated care. A variety of government and specialty society policy initiatives are promoting the adoption of information technologies to engage patients in their care, such as personal health records, but current systems may not utilize the technology's full potential.</p> <p>Methods</p> <p>Using a previously described model to make information technology more patient-centered, we developed an interactive preventive health record (IPHR) designed to more deeply engage patients in preventive care and health promotion. We recruited 14 primary care practices to promote the IPHR to all adult patients and sought practice and patient input in designing the IPHR to ensure its usability, salience, and generalizability. The input involved patient usability tests, practice workflow observations, learning collaboratives, and patient feedback. Use of the IPHR was measured using practice appointment and IPHR databases.</p> <p>Results</p> <p>The IPHR that emerged from this process generates tailored patient recommendations based on guidelines from the U.S. Preventive Services Task Force and other organizations. It extracts clinical data from the practices' electronic medical record and obtains health risk assessment information from patients. Clinical content is translated and explained in lay language. Recommendations review the benefits and uncertainties of services and possible actions for patients and clinicians. Embedded in recommendations are self management tools, risk calculators, decision aids, and community resources - selected to match patient's clinical circumstances. Within six months, practices had encouraged 14.4% of patients to use the IPHR (ranging from 1.5% to 28.3% across the 14 practices). Practices successfully incorporated the IPHR into workflow, using it to prepare patients for visits, augment health behavior counseling, explain test results, automatically issue patient reminders for overdue services, prompt clinicians about needed services, and formulate personalized prevention plans.</p> <p>Conclusions</p> <p>The IPHR demonstrates that a patient-centered personal health record that interfaces with the electronic medical record can give patients a high level of individualized guidance and be successfully adopted by busy primary care practices. Further study and refinement are necessary to make information systems even more patient-centered and to demonstrate their impact on care.</p> <p>Trial Registration</p> <p>Clinicaltrials.gov identifier: <a href="http://www.clinicaltrials.gov/ct2/show/NCT00589173">NCT00589173</a></p

Zafirlukast Is a Dual Modulator of Human Soluble Epoxide Hydrolase and Peroxisome Proliferator-Activated Receptor γ

Cysteinyl leukotriene receptor 1 antagonists (CysLT1RA) are frequently used as add-on medication for the treatment of asthma. Recently, these compounds have shown protective effects in cardiovascular diseases. This prompted us to investigate their influence on soluble epoxide hydrolase (sEH) and peroxisome proliferator activated receptor (PPAR) activities, two targets known to play an important role in CVD and the metabolic syndrome. Montelukast, pranlukast and zafirlukast inhibited human sEH with IC50 values of 1.9, 14.1, and 0.8 μM, respectively. In contrast, only montelukast and zafirlukast activated PPARγ in the reporter gene assay with EC50 values of 1.17 μM (21.9% max. activation) and 2.49 μM (148% max. activation), respectively. PPARα and δ were not affected by any of the compounds. The activation of PPARγ was further investigated in 3T3-L1 adipocytes. Analysis of lipid accumulation, mRNA and protein expression of target genes as well as PPARγ phosphorylation revealed that montelukast was not able to induce adipocyte differentiation. In contrast, zafirlukast triggered moderate lipid accumulation compared to rosiglitazone and upregulated PPARγ target genes. In addition, we found that montelukast and zafirlukast display antagonistic activities concerning recruitment of the PPARγ cofactor CBP upon ligand binding suggesting that both compounds act as PPARγ modulators. In addition, zafirlukast impaired the TNFα triggered phosphorylation of PPARγ2 on serine 273. Thus, zafirlukast is a novel dual sEH/PPARγ modulator representing an excellent starting point for the further development of this compound class