An objective comparison of cell-tracking algorithms

We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge

Nucleus segmentation : towards automated solutions

Single nucleus segmentation is a frequent challenge of microscopy image processing, since it is the first step of many quantitative data analysis pipelines. The quality of tracking single cells, extracting features or classifying cellular phenotypes strongly depends on segmentation accuracy. Worldwide competitions have been held, aiming to improve segmentation, and recent years have definitely brought significant improvements: large annotated datasets are now freely available, several 2D segmentation strategies have been extended to 3D, and deep learning approaches have increased accuracy. However, even today, no generally accepted solution and benchmarking platform exist. We review the most recent single-cell segmentation tools, and provide an interactive method browser to select the most appropriate solution.Peer reviewe

Dirichlet belief networks for topic structure learning

Recently, considerable research effort has been devoted to developing deep architectures for topic models to learn topic structures. Although several deep models have been proposed to learn better topic proportions of documents, how to leverage the benefits of deep structures for learning word distributions of topics has not yet been rigorously studied. Here we propose a new multi-layer generative process on word distributions of topics, where each layer consists of a set of topics and each topic is drawn from a mixture of the topics of the layer above. As the topics in all layers can be directly interpreted by words, the proposed model is able to discover interpretable topic hierarchies. As a self-contained module, our model can be flexibly adapted to different kinds of topic models to improve their modelling accuracy and interpretability. Extensive experiments on text corpora demonstrate the advantages of the proposed model.Comment: accepted in NIPS 201

BlogForever D2.4: Weblog spider prototype and associated methodology

The purpose of this document is to present the evaluation of different solutions for capturing blogs, established methodology and to describe the developed blog spider prototype

Artificial intelligence applications and cataract management: A systematic review

Artificial intelligence (AI)-based applications exhibit the potential to improve the quality and efficiency of patient care in different fields, including cataract management. A systematic review of the different applications of AI-based software on all aspects of a cataract patient's management, from diagnosis to follow-up, was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. All selected articles were analyzed to assess the level of evidence according to the Oxford Centre for Evidence-Based Medicine 2011 guidelines, and the quality of evidence according to the Grading of Recommendations Assessment, Development and Evaluation system. Of the articles analyzed, 49 met the inclusion criteria. No data synthesis was possible for the heterogeneity of available data and the design of the available studies. The AI-driven diagnosis seemed to be comparable and, in selected cases, to even exceed the accuracy of experienced clinicians in classifying disease, supporting the operating room scheduling, and intraoperative and postoperative management of complications. Considering the heterogeneity of data analyzed, however, further randomized controlled trials to assess the efficacy and safety of AI application in the management of cataract should be highly warranted

Amelioration of Mitochondrial Bioenergetic Dysfunction in Diabetes Mellitus: Delving into Specialized and Non-specific Therapeutics for the Ailing Heart

Morbidity and mortality of the diabetic population is influenced by many confounding factors, but cardiovascular disease (CVD), remains the leading cause of death. Mitochondrial dysfunction is central in the development of cardiac contractile dysfunction, with decreased mitochondrial bioenergetic function, increased dependence on free fatty acid utilization, and a decrease in glucose utilization having been shown to contribute to contractile dysfunction. Strategies targeting the amelioration of mitochondrial bioenergetic function are attractive for limiting diabetes-induced heart failure, and preserving health-span. The goals of this dissertation were to assess two mitochondrial-centric approaches for the amelioration of mitochondrial and cardiac contractile dysfunction in diabetes mellitus. Our laboratory previously identified microRNA-378a (miR-378a) as a regulator of mitochondrially encoded ATP synthase membrane subunit 6 (mt-ATP6) mRNA, a component of the ATP synthase F0 complex. More recently, a second class of non-coding RNAs, long non-coding RNAs (lncRNA), have been proposed to regulate microRNA activity. LncRNA potassium voltage-gated channel subfamily Q member 1 overlapping transcript 1 (Kcnq1ot1), is predicted to bind miR-378a. Chapter 2 aimed to determine if inhibition of miR-378a could ameliorate cardiac contractile dysfunction in type 2 diabetes mellitus (T2DM), and to ascertain whether Kcnq1ot1 interacts with miR-378a to impact ATP synthase functionality by preserving mt-ATP6 levels. MiR-378a genomic loss, and inhibition by Kcnq1ot1, improved ATP synthase functionality, and preserved cardiac contractile function. Together, Kcnq1ot1 and miR-378a may act as constituents in an axis that regulates mt-ATP6 content. By acting as therapeutic targets, their manipulation may provide benefit to ATP synthase functionality in the heart during T2DM. A second method of ameliorating mitochondrial dysfunction is mitochondrial transplantation. Current literature suggests that mitochondrial transplantation may be of benefit to the diabetic heart. Chapter 3 aimed to assess mitochondrial transplantation as a prophylactic method of treating mitochondrial dysfunction in the diabetic heart. Following mitochondrial transplantation in vivo using ultrasound-guided echocardiography, mitochondrial signal was detectable in at least 30% of the left ventricle myocardium, primarily within and near injection sites. Poor mitochondrial distribution indicated a need for a more focused injection strategy aimed at targeting a cardiac region or segment of interest. Speckle tracking echocardiography has been utilized to evaluate spatial and progressive alterations in the diabetic heart independently, but the spatial and temporal manifestation of cardiac dysfunction remain elusive. Therefore, the objectives of Chapter 4 were to elucidate if cardiac dysfunction associated with T2DM occurs spatially, and if patterns of regional or segmental dysfunction manifest in a temporal fashion. Non-invasive echocardiography datasets were utilized to segregate mice into two pre-determined groups, wild-type and Db/Db, at 5, 12, 20, and 25 weeks. Machine learning was used to identify and rank cardiac regions, segments, and features by their ability to identify cardiac dysfunction. Overall, the Septal region, and the AntSeptum segment, best represented cardiac dysfunction associated with the diabetic state at 5, 20, and 25 weeks, with the AntSeptum also containing the greatest number of features which differed between diabetic and non-diabetic mice. These results suggested that cardiac dysfunction manifests in a spatial and temporal fashion, and is defined by patterns of regional and segmental dysfunction in the diabetic heart. Further, the Septal region, and AntSeptum segment, may provide a locale of interest for therapeutic interventions aimed at ameliorating cardiac dysfunction in T2DM

Artificial intelligence in construction asset management: a review of present status, challenges and future opportunities

The built environment is responsible for roughly 40% of global greenhouse emissions, making the sector a crucial factor for climate change and sustainability. Meanwhile, other sectors (like manufacturing) adopted Artificial Intelligence (AI) to solve complex, non-linear problems to reduce waste, inefficiency, and pollution. Therefore, many research efforts in the Architecture, Engineering, and Construction community have recently tried introducing AI into building asset management (AM) processes. Since AM encompasses a broad set of disciplines, an overview of several AI applications, current research gaps, and trends is needed. In this context, this study conducted the first state-of-the-art research on AI for building asset management. A total of 578 papers were analyzed with bibliometric tools to identify prominent institutions, topics, and journals. The quantitative analysis helped determine the most researched areas of AM and which AI techniques are applied. The areas were furtherly investigated by reading in-depth the 83 most relevant studies selected by screening the articles’ abstracts identified in the bibliometric analysis. The results reveal many applications for Energy Management, Condition assessment, Risk management, and Project management areas. Finally, the literature review identified three main trends that can be a reference point for future studies made by practitioners or researchers: Digital Twin, Generative Adversarial Networks (with synthetic images) for data augmentation, and Deep Reinforcement Learning