Generating Information Relation Matrix Using Semantic Patent Mining for Technology Planning: A Case of Nano-Sensor

For the purposes of technology planning and research and development strategy development, we present a semi-automated method that extracts text information from patent data, uses natural language processing to extract the key technical information of the patent, and then visualizes this information in a matrix form. We tried to support qualitative analysis of patent contents by extracting functions, components, and contexts, which are the most important information about inventions. We validated the method by applying it to patent data related to nanosensors. The matrix can emphasize technical information that have not been exploited in patents, and thereby identify development opportunities.111Ysciescopu

Sentence Embedding Approach using LSTM Auto-encoder for Discussion Threads Summarization

Online discussion forums are repositories of valuable information where users interact and articulate their ideas and opinions, and share experiences about numerous topics. These online discussion forums are internet-based online communities where users can ask for help and find the solution to a problem. A new user of online discussion forums becomes exhausted from reading the significant number of irrelevant replies in a discussion. An automated discussion thread summarizing system (DTS) is necessary to create a candid view of the entire discussion of a query. Most of the previous approaches for automated DTS use the continuous bag of words (CBOW) model as a sentence embedding tool, which is poor at capturing the overall meaning of the sentence and is unable to grasp word dependency. To overcome these limitations, we introduce the LSTM Auto-encoder as a sentence embedding technique to improve the performance of DTS. The empirical result in the context of the proposed approach’s average precision, recall, and F-measure with respect to ROGUE-1 and ROUGE-2 of two standard experimental datasets demonstrates the effectiveness and efficiency of the proposed approach and outperforms the state-of-the-art CBOW model in sentence embedding tasks and boost the performance of the automated DTS model

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Evaluating Electrode-Tissue Contact Force Using the Moving Pattern of the Catheter Tip and the Electrogram Characteristics

As an important reference for the physician during catheter ablation, the electrode-tissue contact force (CF), is one of the key points for the success of the catheter ablation. With the guide of CF sensing, the ablation procedure can be safer and more effective. Techniques and apparatus have been refined since catheter ablation was invented to treat cardiac arrhythmia. In the review part, different techniques for evaluating the electrode-tissue CF are discussed, including both direct and indirect measurement. Sensor-based direct measurement is broadly applied but restricted by the high cost. Surrogate markers of catheter-tissue contact such as impedance, electrogram (EGM) quality, catheter tip temperature and so on, are taken as reference evaluating CF as well, but each of them has their own drawbacks. In this dissertation, our approach estimating the CF is based on the moving pattern of the catheter tip in the heart chamber. The factors determining the catheter tip motion, include the cardiac and respiratory cycles, blood flow, and so on. If the position of the catheter tip can be recorded, then the motion of the catheter tip can be tracked and analyzed. Based on our collected data, the moving pattern of the catheter tip is different when the electrode-tissue CF level varies. Features extracted from catheter tip motion are significant for CF evaluation. There are different features selected to describe the moving pattern of the catheter tip, which are identified to best represent the movement by checking the corresponding CF as reference. In summary, if the feature has a strong correlation with the CF, then it can be taken as a good feature. Using the features as input, the CF evaluating mechanism is based on a multi-class classification decision tree to make an optimum and comprehensive estimation

Quantitative Cardiac Magnetic Resonance Imaging Biomarkers for the Characterisation of Ischaemic Cardiomyopathy

Our understanding of the processes that determine outcomes in patients with ischaemic cardiomyopathy is based on conventional physiological concepts such as ischaemia and viability. Qualitative methods for characterising these processes tend to be binary and often fail to capture the complexity of the underlying biology. Importantly, these are perhaps inadequate to evaluate treatment effects, including the impact of coronary revascularisation. The aim of this thesis was to deploy novel quantitative cardiac magnetic resonance (CMR) techniques to evaluate and distinguish between the pathophysiological processes that determine outcomes in patients with ischaemic cardiomyopathy, through integration of anatomical, functional, perfusion and tissue characterisation information. The work is centred around the use of coronary artery bypass graft (CABG) surgery as the method for revascularisation, and focuses on the impact of myocardial blood flow alterations on cardiac physiology and clinical outcomes. In this work, I first evaluate the impact of surgical revascularisation on myocardial structure and function in patients with impaired left ventricular (LV) systolic function, using paired assessments before and after CABG. I found that at 6 months following revascularisation, despite improvement in functional capacity, more than a third of total myocardial segments examined are no longer considered revascularised. As a result, the overall augmentation in global myocardial blood flow (MBF) following CABG surgery is significantly blunted. There are however technical concerns regarding the quantitative estimation of myocardial blood flow in patients with coronary artery grafts, particularly in relation to the impact of long coronary grafts on contrast kinetics. I therefore evaluated the impact of arterial contrast delay on myocardial blood flow estimation in patients with left internal mammary artery (LIMA) grafts. I showed that absolute MBF estimation is minimally affected by delayed contrast arrival in patients with LIMA grafts, and that irrespective of graft patency, residual native disease severity is a key determinant of myocardial blood flow. Following these findings, I then assessed the prognostic impact of myocardial blood flow in a large cohort of patients with prior CABG. The only imaging study to date examining the prognostic role of quantitative perfusion indices in this population, it demonstrated that both stress MBF and myocardial perfusion reserve (MPR) independently predict adverse cardiovascular outcomes and all cause-mortality. Finally, using the existing quantitative perfusion technique and its associated framework, I co-developed and implemented a non-invasive, in-line method of measuring pulmonary transit time (PTT) and pulmonary blood volume (PBV) during routine CMR scanning. I then found that both imaging parameters can be used as independent quantitative prognostic biomarkers in patients with known or suspected coronary artery disease

Computational approaches for translational oncology: Concepts and patents

Background: Cancer is a heterogeneous disease, which is based on an intricate network of processes at different spatiotemporal scales, from the genome to the tissue level. Hence the necessity for the biomedical and pharmaceutical research to work in a multiscale fashion. In this respect, a significant help derives from the collaboration with theoretical sciences. Mathematical models can in fact provide insights into tumor-related processes and support clinical oncologists in the design of treatment regime, dosage, schedule and toxicity. Objective and Method: The main objective of this article is to review the recent computational-based patents which tackle some relevant aspects of tumor treatment. We first analyze a series of patents concerning the purposing the purposing or repurposing of anti-tumor compounds. These approaches rely on pharmacokinetics and pharmacodynamics modules, that incorporate data obtained in the different phases of clinical trials. Similar methods are also at the basis of other patents included in this paper, which deal with treatment optimization, in terms of maximizing therapy efficacy while minimizing side effects on the host. A group of patents predicting drug response and tumor evolution by the use of kinetics graphs are commented as well. We finally focus on patents that implement informatics tools to map and screen biological, medical, and pharmaceutical knowledge. Results and Conclusions: Despite promising aspects (and an increasing amount of the relative literature), we found few computational-based patents: There is still a significant effort to do for allowing modelling approaches to become an integral component of the pharmaceutical research