Democratized image analytics by visual programming through integration of deep models and small-scale machine learning

Analysis of biomedical images requires computational expertize that are uncommon among biomedical scientists. Deep learning approaches for image analysis provide an opportunity to develop user-friendly tools for exploratory data analysis. Here, we use the visual programming toolbox Orange (http://orange.biolab.si) to simplify image analysis by integrating deep-learning embedding, machine learning procedures, and data visualization. Orange supports the construction of data analysis workflows by assembling components for data preprocessing, visualization, and modeling. We equipped Orange with components that use pre-trained deep convolutional networks to profile images with vectors of features. These vectors are used in image clustering and classification in a framework that enables mining of image sets for both novel and experienced users. We demonstrate the utility of the tool in image analysis of progenitor cells in mouse bone healing, identification of developmental competence in mouse oocytes, subcellular protein localization in yeast, and developmental morphology of social amoebae

Control of aggregation temperatures in mixed and blended cytocompatible thermoresponsive block co-polymer nanoparticles

A small library of thermoresponsive amphiphilic copolymers based on polylactide-block-poly((2-(2-methoxyethoxy)ethyl methacrylate)-co-(oligoethylene glycol methacrylate)) (PLA-b-P(DEGMA)-co-(OEGMA)), was synthesised by copper-mediated controlled radical polymerisation (CRP) with increasing ratios of OEGMA:DEGMA. These polymers were combined in two ways to form nanoparticles with controllable thermal transition temperatures as measured by particle aggregation. The first technique involved the blending of two (PLA-b-P(DEGMA)-co-(OEGMA)) polymers together prior to assembling NPs. The second method involved mixing pre-formed nanoparticles of single (PLA-b-P(DEGMA)-co-(OEGMA)) polymers. The observed critical aggregation temperature Tt did not change in a linear relationship with the ratios of each copolymer either in the nanoparticles blended from different copolymers or in the mitures of pre-formed nanoparticles. However, where co-polymer mixtures were based on (OEG)9MA ratios within 5-10 mole% , a linear relationship between (OEG)9MA composition in the blends and Tt was obtained. The data suggest that OEGMA-based copolymers are tunable over a wide temperature range given suitable co-monomer content in the linear polymers or nanoparticles. Moreover, the thermal transitions of the nanoparticles were reversible and repeatable, with the cloud point curves being essentially invariant across at least three heating and cooling cycles, and a selected nanoparticle formulation was found to be readily endocytosed in representative cancer cells and fibroblasts

BUILDING BRIDGES FOR INNOVATION IN AGEING : SYNERGIES BETWEEN ACTION GROUPS OF THE EIP ON AHA

The Strategic Implementation Plan of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) proposed six Action Groups. After almost three years of activity, many achievements have been obtained through commitments or collaborative work of the Action Groups. However, they have often worked in silos and, consequently, synergies between Action Groups have been proposed to strengthen the triple win of the EIP on AHA. The paper presents the methodology and current status of the Task Force on EIP on AHA synergies. Synergies are in line with the Action Groups' new Renovated Action Plan (2016-2018) to ensure that their future objectives are coherent and fully connected. The outcomes and impact of synergies are using the Monitoring and Assessment Framework for the EIP on AHA (MAFEIP). Eight proposals for synergies have been approved by the Task Force: Five cross-cutting synergies which can be used for all current and future synergies as they consider overarching domains (appropriate polypharmacy, citizen empowerment, teaching and coaching on AHA, deployment of synergies to EU regions, Responsible Research and Innovation), and three cross-cutting synergies focussing on current Action Group activities (falls, frailty, integrated care and chronic respiratory diseases).Peer reviewe

The comparative responsiveness of Hospital Universitario Princesa Index and other composite indices for assessing rheumatoid arthritis activity

Objective To evaluate the responsiveness in terms of correlation of the Hospital Universitario La Princesa Index (HUPI) comparatively to the traditional composite indices used to assess disease activity in rheumatoid arthritis (RA), and to compare the performance of HUPI-based response criteria with that of the EULAR response criteria. Methods Secondary data analysis from the following studies: ACT-RAY (clinical trial), PROAR (early RA cohort) and EMECAR (pre-biologic era long term RA cohort). Responsiveness was evaluated by: 1) comparing change from baseline (Delta) of HUPI with Delta in other scores by calculating correlation coefficients; 2) calculating standardised effect sizes. The accuracy of response by HUPI and by EULAR criteria was analyzed using linear regressions in which the dependent variable was change in global assessment by physician (Delta GDA-Phy). Results Delta HUPI correlation with change in all other indices ranged from 0.387 to 0.791); HUPI's standardized effect size was larger than those from the other indices in each database used. In ACT-RAY, depending on visit, between 65 and 80% of patients were equally classified by HUPI and EULAR response criteria. However, HUPI criteria were slightly more stringent, with higher percentage of patients classified as non-responder, especially at early visits. HUPI response criteria showed a slightly higher accuracy than EULAR response criteria when using Delta GDA-Phy as gold standard. Conclusion HUPI shows good responsiveness in terms of correlation in each studied scenario (clinical trial, early RA cohort, and established RA cohort). Response criteria by HUPI seem more stringent than EULAR''s

Imaging biomarker roadmap for cancer studies.

Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing 'translational gaps' through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored 'roadmap'. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use.Development of this roadmap received support from Cancer Research UK and the Engineering and Physical Sciences Research Council (grant references A/15267, A/16463, A/16464, A/16465, A/16466 and A/18097), the EORTC Cancer Research Fund, and the Innovative Medicines Initiative Joint Undertaking (grant agreement number 115151), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and European Federation of Pharmaceutical Industries and Associations (EFPIA) companies' in kind contribution