Background: Exercise training has been extensively studied in heart failure (HF) and psychological disorders, which has been shown to worsen each other. However, our understanding of how exercise simultaneously protect heart and brain of HF patients is still in its infancy. The purpose of this study was to take advantage of big data techniques to explore hotspots and frontiers of mechanisms that protect the heart and brain simultaneously through exercise training.Methods: We studied the scientific publications on related research between January 1, 2003 to December 31, 2020 from the WoS Core Collection. Research hotspots were assessed through open-source software, CiteSpace, Pajek, and VOSviewer. Big data analysis and visualization were carried out using R, Cytoscape and Origin.Results: From 2003 to 2020, the study on HF, depression, and exercise simultaneously was the lowest of all research sequences (two-way ANOVAs, p < 0.0001). Its linear regression coefficient r was 0.7641. The result of hotspot analysis of related keyword-driven research showed that inflammation and stress (including oxidative stress) were the common mechanisms. Through the further analyses, we noted that inflammation, stress, oxidative stress, apoptosis, reactive oxygen species, cell death, and the mechanisms related to mitochondrial biogenesis/homeostasis, could be regarded as the primary mechanism targets to study the simultaneous intervention of exercise on the heart and brain of HF patients with depression.Conclusions: Our findings demonstrate the potential mechanism targets by which exercise interferes with both the heart and brain for HF patients with depression. We hope that they can boost the attention of other researchers and clinicians, and open up new avenues for designing more novel potential drugs to block heart-brain axis vicious circle.

Background: Although over a dozen disease modifying treatments (DMTs) are available for relapsing forms of multiple sclerosis (MS), treatment interruption, switching and discontinuation are common challenges. The objective of this study was to describe treatment interruption and discontinuation in the Big MS data network.Methods: We merged information on 269,822 treatment episodes in 110,326 patients from 1997 to 2016 from five clinical registries in this cohort study. Treatment stop was defined as a clinician recorded DMT end for any reason and included treatment interruptions, switching to alternate DMTs and long-term or permanent discontinuations.Results: The incidence of DMT stopping cross the full observation period was lowest in FTY (19.7 per 100 person-years (PY) of treatment; 95% CI 19.2–20.1), followed by NAT (22.6/100 PY; 95% CI 22.2–23.0), IFNβ (23.3/100 PY; 95% CI 23.2–23.5). Of the 184,013 observed DMT stops, 159,309 (86.6%) switched to an alternate DMT within 6 months. Reasons for stopping a drug were stable during the observation period with lack of efficacy being the most common reason followed by lack of tolerance and side effects. The proportion of patients continuing on most DMTs were similarly stable until 2014 and 2015 when drop from 83 to 75% was noted.Conclusions: DMT stopping reasons and rates were mostly stable over time with a slight increase in recent years, with the availability of more DMTs. The overall results suggest that discontinuation of MS DMTs is mostly due to DMT properties and to a lesser extent to risk management and a competitive market.

Given the current explosion of information over various media such as electronic and physical texts, concise and relevant data has become key to the understanding of things. Summarization, which essentially is the process of reducing the text to convey only the salient aspects, has emerged as a challenging task in the field of Natural Language Processing. In a scientific construct, academia has been generating voluminous amounts of data in the form of theses and dissertations. Obtaining the chapter-wise summary of an electronic thesis or dissertation can be a computationally expensive task, particularly because of its length and the subject to which it pertains to. Through this course, research and development of various summarization techniques, primarily extractive and abstractive summarization, were analyzed. There have been various developments in the field of deep learning to tackle problems related to summarization and produce coherent and meaningful summaries for news articles. In this project, tools that could be used to generate coherent and concise summaries of long electronic theses and dissertations (ETDs) were developed as well. The major concern initially was to get the text from a PDF file of an ETD. GROBID and Scienceparse were used as pre-processing tools to carry out this task and presented the text from a PDF in a structured format such as XML or JSON file. The outputs from each of the tools were compared qualitatively as well as quantitatively. After this, a transfer learning approach was adopted, wherein a pre-trained model was tweaked to fit to the task of summarizing each ETD. This came in as a challenge to make the model learn the nuances of an ETD. An iterative approach was used to explore various networks, each trying to improve the shortcomings of the previous one in its novel way. Existing deep learning models including Sequence-2-Sequence, Pointer Generator Networks, and A Hybrid Extractive-Abstractive Reinforce-Selecting Sentence Rewriting Network, were used to generate and test summaries. Further tweaks were made to these deep neural networks to account for much longer and varied datasets as compared to what they were inherently designed to work for -- in this case ETDs. A thorough evaluation of these generated summaries was also done with respect to golden standards for five dissertations and theses created during the span of the course. ROUGE-1, ROUGE-2, and ROUGE-SU4 were used to compare the generated summaries with the golden standards. The average ROUGE scores were 0.1387, 0.1224, and 0.0480 respectively. These low ROUGE scores could be attributed to the varying summary length, and also to the complexity of the task of summarizing an ETD. The scope of improvements and the underlying reasons for the performance have also been analyzed. The conclusion that can be drawn from the project is that any machine learning task is highly biased by what pattern is inherently present in the data on which it is being trained. In the context of summarization, there can be a different perspective from which an article can be summarized, and thus the quantitative evaluation measures can vary drastically even after the summary is a coherent one. NSF: IIS-1619028 The submission contains multiple files: - CS5984_Final_Presentation.pdf: The PDF version of the presentation. - CS5984_Final_Presentation.ppt: The PowerPoint for the presentation. - CS5984_Final_Report.pdf: The PDF version of the report. - CS5984_Final_Report.zip: The LaTeX source code for the report. - ArXiv finished file: processed and tokenized arXiv data for Pointer Generator Network -text-summarization-tensorflow: seq2seq model code in TensorFlow modified to adapt with arXiv dataset

The age of big data has fueled expectations for accelerating learning. The availability of large data sets enables researchers to achieve more powerful statistical analyses and enhances the reliability of conclusions, which can be based on a broad collection of subjects. Often such data sets can be assembled only with access to diverse sources; for example, medical research that combines data from multiple centers in a federated analysis. However these hopes must be balanced against data privacy concerns, which hinder sharing raw data among centers. Consequently, federated analyses typically resort to sharing data summaries from each center. The limitation to summaries carries the risk that it will impair the efficiency of statistical analysis procedures. In this work, we take a close look at the effects of federated analysis on two very basic problems, non-parametric comparison of two groups and quantile estimation to describe the corresponding distributions. We also propose a specific privacy-preserving data release policy for federated analysis with the K-anonymity criterion, which has been adopted by the Medical Informatics Platform of the European Human Brain Project. Our results show that, for our tasks, there is only a modest loss of statistical efficiency.

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Artificial Intelligence Platforms Market Size 2024-2028

The artificial intelligence platforms market size is forecast to increase by USD 64.9 billion at a CAGR of 45.1% between 2023 and 2028. The market is experiencing significant growth due to the rising demand for AI-based solutions in various industries. Businesses are increasingly adopting AI technologies to automate processes, enhance productivity, and improve customer experiences. Another trend driving AI platforms market growth is the increasing interoperability among neural networks, enabling seamless data exchange and collaboration between different AI systems. However, the market also faces challenges such as the rise in data privacy issues and ethical concerns related to AI usage. As data becomes a valuable asset, ensuring its security and privacy is paramount for businesses implementing AI solutions. This dynamic market landscape underscores the critical role of artificial intelligence platforms in driving innovation and efficiency across various sectors such as education and telecommunications. Additionally, there is a need for clear regulations and guidelines to address ethical concerns and ensure transparency in AI decision-making. Overall, the market for artificial intelligence platforms is expected to continue its growth trajectory, driven by these trends and challenges.

What will be the Size of the Artificial Intelligence Platforms Market During the Forecast Period?

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Artificial Intelligence Platforms Market Segmentation

The AI platforms market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018 - 2022 for the following segments.

Deployment Outlook 

  On-premise
  Cloud-based


Application Outlook

  Retail
  Banking
  Manufacturing
  Healthcare
  Others


Region Outlook

  North America

    U.S.
    Canada





  Europe

    U.K.
    Germany
    France
    Rest of Europe





  APAC

    China
    India





  Middle East & Africa

    Saudi Arabia
    South Africa
    Rest of the Middle East & Africa


  South America

    Chile
    Brazil
    Argentina

By Application Insights

The retail segment is estimated to witness significant growth during the forecast period. Artificial intelligence (AI) is revolutionizing various industries by enabling advanced data processing, pattern identification, and decision-making capabilities. In healthcare, AI is used for medical imaging analysis, drug discovery, and patient care. In the food and beverages sector, AI is employed for supply chain optimization and product innovation. Digital technologies, including AI software, are transforming banking by facilitating algorithmic trading, fraud detection, and credit risk assessment.

Industry adoption of AI is also prominent in business intelligence, customer experience, and operational efficiency. The emergence of technologies such as big data, IoT, customer relationship management (CRM), and workflow automation are accelerating technological transformations in the sector. AI is used to provide personalized recommendations, automate processes, and optimize workflows. Intelligent virtual assistants, chatbots, natural language processing, speech recognition, and conversational AI interactions are increasingly being used to enhance customer experience.

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The retail segment accounted for USD 662.60 million in 2018. Industry-specific AI Solutions are being developed for finance, where they are used for regulatory support, ethical considerations, data privacy, and security concerns. AI as a service (AIaaS) and cloud computing platforms are enabling businesses to leverage AI capabilities without having to build and maintain their own infrastructure.

Autonomous systems are being adopted for process optimization in manufacturing and logistics. In conclusion, AI is transforming industries by enabling advanced data processing, pattern identification, and decision-making capabilities. Its applications include healthcare, food and beverages, banking, business intelligence, customer experience, and operational efficiency. AI is also being used to develop industry-specific solutions for finance, and to enable autonomous systems for process optimization. Despite the numerous benefits, ethical considerations, data privacy, and security concerns remain key challenges.

Regional Analysis

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North America is estimated to contribute 66% to the growth of the global artificial intelligence platforms market during the market forecast period. Technavio's analysts have elaborately explained the regional trends an