Deconvolution of 1D NMR spectra : a deep learning-based approach

The analysis of nuclear magnetic resonance (NMR) spectra to detect peaks and characterize their parameters, often referred to as deconvolution, is a crucial step in the quantification, elucidation, and verification of the structure of molecular systems. However, deconvolution of 1D NMR spectra is a challenge for both experts and machines. We propose a robust, expert-level quality deep learning-based deconvolution algorithm for 1D experimental NMR spectra. The algorithm is based on a neural network trained on synthetic spectra. Our customized pre-processing and labeling of the synthetic spectra enable the estimation of critical peak parameters. Furthermore, the neural network model transfers well to the experimental spectra and demonstrates low fitting errors and sparse peak lists in challenging scenarios such as crowded, high dynamic range, shoulder peak regions as well as broad peaks. We demonstrate in challenging spectra that the proposed algorithm is superior to expert results

The influence of bisphosphonates on human osteoblast migration and integrin aVb3/tenascin C gene expression in vitro

<p>Abstract</p> <p>Background</p> <p>Bisphosphonates are therapeutics of bone diseases, such as Paget's disease, multiple myeloma or osteoclastic metastases. As a severe side effect the bisphosphonate induced osteonecrosis of the jaw (BONJ) often requires surgical treatment and is accompanied with a disturbed wound healing.</p> <p>Therefore, the influence on adhesion and migration of human osteoblasts (hOB) after bisphosphonate therapy has been investigated by morphologic as well as gene expression methods.</p> <p>Methods</p> <p>By a scratch wound experiment, which measures the reduction of defined cell layer gap, the morphology and migration ability of hOB was evaluated. A test group of hOB, which was stimulated by zoledronate 5 × 10^-5M, and a control group of unstimulated hOB were applied. Furthermore the gene expression of integrin aVb3 and tenascin C was quantified by Real-Time rtPCR at 5data points over an experimental period of 14 days. The bisphosphonates zoledronate, ibandronate and clodronate have been compared with an unstimulated hOB control.</p> <p>Results</p> <p>After initially identical migration and adhesion characteristics, zoledronate inhibited hOB migration after 50 h of stimulation. The integrinavb3 and tenascin C gene expression was effected by bisphosphonates in a cell line dependent manner with decreased, respectively inconsistent gene expression levels over time. The non-nitrogen containing bisphosphonates clodronate led to decreased gene expression levels.</p> <p>Conclusion</p> <p>Bisphosphonates seem to inhibit hOB adhesion and migration. The integrin aVb3 and tenascin C gene expression seem to be dependent on the cell line. BONJ could be enhanced by an inhibition of osteoblast adhesion and migration. The gene expression results, however, suggest a cell line dependent effect of bisphosphonates, which could explain the interindividual differences of BONJ incidences.</p

A Bayesian Nonparametric Approach to Modeling Motion Patterns

The most difficult—and often most essential— aspect of many interception and tracking tasks is constructing motion models of the targets to be found. Experts can often provide only partial information, and fitting parameters for complex motion patterns can require large amounts of training data. Specifying how to parameterize complex motion patterns is in itself a difficult task. In contrast, nonparametric models are very flexible and generalize well with relatively little training data. We propose modeling target motion patterns as a mixture of Gaussian processes (GP) with a Dirichlet process (DP) prior over mixture weights. The GP provides a flexible representation for each individual motion pattern, while the DP assigns observed trajectories to particular motion patterns. Both automatically adjust the complexity of the motion model based on the available data. Our approach outperforms several parametric models on a helicopter-based car-tracking task on data collected from the greater Boston area

The Pioneer Anomaly

Radio-metric Doppler tracking data received from the Pioneer 10 and 11 spacecraft from heliocentric distances of 20-70 AU has consistently indicated the presence of a small, anomalous, blue-shifted frequency drift uniformly changing with a rate of ~6 x 10^{-9} Hz/s. Ultimately, the drift was interpreted as a constant sunward deceleration of each particular spacecraft at the level of a_P = (8.74 +/- 1.33) x 10^{-10} m/s^2. This apparent violation of the Newton's gravitational inverse-square law has become known as the Pioneer anomaly; the nature of this anomaly remains unexplained. In this review, we summarize the current knowledge of the physical properties of the anomaly and the conditions that led to its detection and characterization. We review various mechanisms proposed to explain the anomaly and discuss the current state of efforts to determine its nature. A comprehensive new investigation of the anomalous behavior of the two Pioneers has begun recently. The new efforts rely on the much-extended set of radio-metric Doppler data for both spacecraft in conjunction with the newly available complete record of their telemetry files and a large archive of original project documentation. As the new study is yet to report its findings, this review provides the necessary background for the new results to appear in the near future. In particular, we provide a significant amount of information on the design, operations and behavior of the two Pioneers during their entire missions, including descriptions of various data formats and techniques used for their navigation and radio-science data analysis. As most of this information was recovered relatively recently, it was not used in the previous studies of the Pioneer anomaly, but it is critical for the new investigation.Comment: 165 pages, 40 figures, 16 tables; accepted for publication in Living Reviews in Relativit

MR fluoroscopy in vascular and cardiac interventions (review)

Vascular and cardiac disease remains a leading cause of morbidity and mortality in developed and emerging countries. Vascular and cardiac interventions require extensive fluoroscopic guidance to navigate endovascular catheters. X-ray fluoroscopy is considered the current modality for real time imaging. It provides excellent spatial and temporal resolution, but is limited by exposure of patients and staff to ionizing radiation, poor soft tissue characterization and lack of quantitative physiologic information. MR fluoroscopy has been introduced with substantial progress during the last decade. Clinical and experimental studies performed under MR fluoroscopy have indicated the suitability of this modality for: delivery of ASD closure, aortic valves, and endovascular stents (aortic, carotid, iliac, renal arteries, inferior vena cava). It aids in performing ablation, creation of hepatic shunts and local delivery of therapies. Development of more MR compatible equipment and devices will widen the applications of MR-guided procedures. At post-intervention, MR imaging aids in assessing the efficacy of therapies, success of interventions. It also provides information on vascular flow and cardiac morphology, function, perfusion and viability. MR fluoroscopy has the potential to form the basis for minimally invasive image–guided surgeries that offer improved patient management and cost effectiveness

Carbon Dioxide Utilisation -The Formate Route

UIDB/50006/2020 CEEC-Individual 2017 Program Contract.The relentless rise of atmospheric CO2 is causing large and unpredictable impacts on the Earth climate, due to the CO2 significant greenhouse effect, besides being responsible for the ocean acidification, with consequent huge impacts in our daily lives and in all forms of life. To stop spiral of destruction, we must actively reduce the CO2 emissions and develop new and more efficient “CO2 sinks”. We should be focused on the opportunities provided by exploiting this novel and huge carbon feedstock to produce de novo fuels and added-value compounds. The conversion of CO2 into formate offers key advantages for carbon recycling, and formate dehydrogenase (FDH) enzymes are at the centre of intense research, due to the “green” advantages the bioconversion can offer, namely substrate and product selectivity and specificity, in reactions run at ambient temperature and pressure and neutral pH. In this chapter, we describe the remarkable recent progress towards efficient and selective FDH-catalysed CO2 reduction to formate. We focus on the enzymes, discussing their structure and mechanism of action. Selected promising studies and successful proof of concepts of FDH-dependent CO2 reduction to formate and beyond are discussed, to highlight the power of FDHs and the challenges this CO2 bioconversion still faces.publishersversionpublishe

Risk-averse policy optimization via risk-neutral policy optimization

Keeping risk under control is a primary objective in many critical real-world domains, including finance and healthcare. The literature on risk-averse reinforcement learning (RL) has mostly focused on designing ad-hoc algorithms for specific risk measures. As such, most of these algorithms do not easily generalize to measures other than the one they are designed for. Furthermore, it is often unclear whether state-of-the-art risk-neutral RL algorithms can be extended to reduce risk. In this paper, we take a step towards overcoming these limitations, proposing a single framework to optimize some of the most popular risk measures, including conditional value-at-risk, utility functions, and mean-variance. Leveraging recent theoretical results on state augmentation, we transform the decision-making process so that optimizing the chosen risk measure in the original environment is equivalent to optimizing the expected cost in the transformed one. We then present a simple risk-sensitive meta-algorithm that transforms the trajectories it collects from the environment and feeds these into any risk-neutral policy optimization method. Finally, we provide extensive experiments that show the benefits of our approach over existing ad-hoc methodologies in different domains, including the Mujoco robotic suite and a real-world trading dataset