Clinical characteristics of subsequent histologically confirmed meningiomas in long-term childhood cancer survivors:A Dutch LATER study

Background: Meningiomas are the most frequent brain tumours occurring after pediatric cranial radiotherapy (CrRT). Data on course of disease, to inform clinical management of meningiomas, are sparse. This study reports the clinical characteristics of histologically confirmed meningiomas in childhood cancer survivors (CCS) in the Netherlands.& nbsp; Methods: In total, 6015 CCS from the Dutch Long-Term Effects After Childhood Cancer (LATER) cohort were eligible, including 1551 with prior CrRT. These CCS were diagnosed with cancer ag

Exploring data provenance in handwritten text recognition infrastructure:Sharing and reusing ground truth data, referencing models, and acknowledging contributions. Starting the conversation on how we could get it done

This paper discusses best practices for sharing and reusing Ground Truth in Handwritten Text Recognition infrastructures, and ways to reference and acknowledge contributions to the creation and enrichment of data within these Machine Learning systems. We discuss how one can publish Ground Truth data in a repository and, subsequently, inform others. Furthermore, we suggest appropriate citation methods for HTR data, models, and contributions made by volunteers. Moreover, when using digitised sources (digital facsimiles), it becomes increasingly important to distinguish between the physical object and the digital collection. These topics all relate to the proper acknowledgement of labour put into digitising, transcribing, and sharing Ground Truth HTR data. This also points to broader issues surrounding the use of Machine Learning in archival and library contexts, and how the community should begin toacknowledge and record both contributions and data provenance

Exploring Data Provenance in Handwritten Text Recognition Infrastructure: Sharing and Reusing Ground Truth Data, Referencing Models, and Acknowledging Contributions. Starting the Conversation on How We Could Get It Done

This paper discusses best practices for sharing and reusing Ground Truth in Handwritten Text Recognition infrastructures, as well as ways to reference and acknowledge contributions to the creation and enrichment of data within these systems. We discuss how one can place Ground Truth data in a repository and, subsequently, inform others through HTR-United. Furthermore, we want to suggest appropriate citation methods for ATR data, models, and contributions made by volunteers. Moreover, when using digitised sources (digital facsimiles), it becomes increasingly important to distinguish between the physical object and the digital collection. These topics all relate to the proper acknowledgement of labour put into digitising, transcribing, and sharing Ground Truth HTR data. This also points to broader issues surrounding the use of machine learning in archival and library contexts, and how the community should begin to acknowledge and record both contributions and data provenance

Combined Inactivation of pRB and Hippo Pathways Induces Dedifferentiation in the Drosophila Retina

Functional inactivation of the Retinoblastoma (pRB) pathway is an early and obligatory event in tumorigenesis. The importance of pRB is usually explained by its ability to promote cell cycle exit. Here, we demonstrate that, independently of cell cycle exit control, in cooperation with the Hippo tumor suppressor pathway, pRB functions to maintain the terminally differentiated state. We show that mutations in the Hippo signaling pathway, wts or hpo, trigger widespread dedifferentiation of rbf mutant cells in the Drosophila eye. Initially, rbf wts or rbf hpo double mutant cells are morphologically indistinguishable from their wild-type counterparts as they properly differentiate into photoreceptors, form axonal projections, and express late neuronal markers. However, the double mutant cells cannot maintain their neuronal identity, dedifferentiate, and thus become uncommitted eye specific cells. Surprisingly, this dedifferentiation is fully independent of cell cycle exit defects and occurs even when inappropriate proliferation is fully blocked by a de2f1 mutation. Thus, our results reveal the novel involvement of the pRB pathway during the maintenance of a differentiated state and suggest that terminally differentiated Rb mutant cells are intrinsically prone to dedifferentiation, can be converted to progenitor cells, and thus contribute to cancer advancement

Covered stents versus Bare-metal stents in chronic atherosclerotic Gastrointestinal Ischemia (CoBaGI): Study protocol for a randomized controlled trial

Background: Chronic mesenteric ischemia (CMI) is the result of insufficient blood supply to the gastrointestinal tract and is caused by atherosclerotic stenosis of one or more mesenteric arteries in > 90% of cases. Revascularization therapy is indicated in patients with a diagnosis of atherosclerotic CMI to relieve symptoms and to prevent acute-on-chronic mesenteric ischemia, which is associated with high morbidity and mortality. Endovascular therapy has rapidly evolved and has replaced surgery as the first choice of treatment in CMI. Bare-metal stents (BMS) are standard care currently, although retrospective studies suggested significantly highe

A robust cell cycle control mechanism limits E2F-induced proliferation of terminally differentiated cells in vivo

Overexpression of both CycE and E2F is necessary to trigger cell cycle reentry and overproliferation of terminally differentiated wing cells

Automated food safety early warning system in the dairy supply chain using machine learning

Traditionally, early warning systems for food safety are based on monitoring targeted food safety hazards. Optimal early warning systems, however, should identify signals that precede the development of a food safety risk. Moreover, such signals could be identified in factors from domains adjacent to the food supply chain, so-called drivers of change and other indicators. In this study, we show for the first time that such drivers and indicators may indeed represent signals that precede the detection of a food safety risk. The dairy supply chain in Europe was used as an application case. Using dynamic unsupervised anomaly detection models, anomalies were detected in indicator data expected by domain experts to impact the development of food safety risks in milk. Additionally, a Bayesian network was used to identify the chemical food safety hazards in milk associated with an anomaly for the Netherlands. The results showed that the frequency of anomalies varied per country and indicator. However, all countries showed in the period investigated (2008–2019), anomalies in the indicators “raw milk price” and “barely milk price” and no anomalies in the indicator” income of dairy farms”. A cross-correlation analysis of the number of Rapid Alert for Food and Feed (RASFF) notifications and anomalies in indicators revealed significant correlations of many indicators but difference between countries was observed. Interesting, for all countries the cross corelation with indicator “milk price” was significant, albeit the lag time varied from 5 months (United Kingdom) to 22 months (Italy). This finding suggests that severe changes in domains adjacent to the food supply chain may trigger the development of food safety problems that become visible many months later. Awareness of such relationships will provide the opportunity for food producers or inspectors to take timely measures to prevent food safety problems

Automatic classification of literature in systematic reviews on food safety using machine learning

Systematic reviews are used to collect relevant literature to answer a research question in a way that is clear, thorough, unbiased and reproducible. They are implemented as a standard method in the domain of food safety to obtain a literature overview on the state-of-the-art research related to food safety topics of interest. A disadvantage to systematic reviews, however, is that this process is time-consuming and requires expert domain knowledge. The work reported here aims to reduce the time needed by an expert to screen all possible relevant articles by applying machine learning techniques to classify the articles automatically as either relevant or not relevant. Eight different machine learning algorithms and ensembles of all combinations of these algorithms were tested on two different systematic reviews on food safety (i.e. chemical hazards in cereals and leafy greens). The results showed that the best performance was obtained by an ensemble of naive Bayes and a support vector machine, resulting in an average decrease of 32.8% in the amount of articles the expert has to read and an average decrease in irrelevant articles of 57.8% while keeping 95% of the relevant articles. It was concluded that automatic classification of the literature in a systematic literature review can support experts in their task and save valuable time without compromising the quality of the review

Artificial intelligence to detect unknown stimulants from scientific literature and media reports

The world market for food supplements is large and is driven by the claims of these products to, for example, treat obesity, increase focus and alertness, decrease appetite, decrease the need for sleep or reduce impulsivity. The use of illegal compounds in food supplements is a continuous threat, certainly because these compounds and products have not been tested for safety by competent authorities. It is therefore of the utmost importance for the competent authorities to know when new products are being marketed and to warn users against potential health risks. In this study, an approach is presented to detect new and unknown stimulants in food supplements using machine learning. Twenty new stimulants were identified from two different data sources, namely scientific literature applying word embedding on > 2 million abstracts and articles from formal and social media on the world wide web using text mining. The results show that the developed approach may be suitable to detect “unknowns” in the emerging risk identification activities performed by the competent authorities, which is currently a major hurdle

Big Data in food safety- A review

The massive rise of Big Data generated from smartphones, social media, Internet of Things (IoT), and multimedia, has produced an overwhelming flow of data in either structured or unstructured format. Big Data technologies are being developed and implemented in the food supply chain that gather and analyse these data. Such technologies demand new approaches in data collection, storage, processing and knowledge extraction. In this article, an overview of the recent developments in Big Data applications in food safety are presented. This review shows that the use of Big Data in food safety remains in its infancy but it is influencing the entire food supply chain. Big Data analysis is used to provide predictive insights in several steps in the food supply chain, support supply chain actors in taking real time decisions, and design the monitoring and sampling strategies. Lastly, the main research challenges that require research efforts are introduced