Production of genetically identical non-human primates through somatic cell nuclear transfer (SCNT) can provide diseased genotypes for research and clarify embryonic stem cell potentials. Understanding the cellular and molecular changes in SCNT is crucial to its success. Thus the changes in the first cell cycle of reconstructed zygotes after nuclear transfer (NT) of somatic cells in the Long-tailed Macaque (Macaca fascicularis) were studied. Embryos were reconstructed by injecting cumulus and fibroblasts from M. fascicularis and M. silenus, into enucleated M. fascicularis oocytes. A spindle of unduplicated premature condensed chromosome (PCC spindle) from the donor somatic cell was formed at 2 hours after NT. Following activation, the chromosomes segregated and moved towards the two PCC spindle poles, then formed two nuclei. Twenty-four hours after activation, the first cell division occurred. A schematic of the first cell cycle changes following injection of a somatic cell into an enucleated oocyte is proposed. Ninety-three reconstructed embryos were transferred into 31 recipients, resulting in 7 pregnancies that were confirmed by ultrasound;unfortunately none progressed beyond 60 days.

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While the digital twins paradigm has attracted the interest of several research communities over the past twenty years, it has also gained ground recently in industrial environments, where mature technologies such as cloud, edge and IoT promise to enable the cost-effective implementation of digital twins. In the industrial manufacturing field, a digital model refers to a virtual representation of a physical product or process that integrates data taken from various sources, such as application program interface (API) data, historical data, embedded sensor data and open data, and that is capable of providing manufacturers with unprecedented insights into the product’s expected performance or the defects that may cause malfunctions. The EU-funded IoTwins project aims to build a solid platform that manufacturers can access to develop hybrid digital twins (DTs) of their assets, deploy them as close to the data origin as possible (on IoT gateway or on edge nodes) and take advantage of cloud-based resources to off-load intensive computational tasks such as, e.g., big data analytics and machine learning (ML) model training. In this paper, we present the main research goals of the IoTwins project and discuss its reference architecture, platform functionalities and building components. Finally, we discuss an industry-related use case that showcases how manufacturers can leverage the potential of the IoTwins platform to develop and execute distributed DTs for the the predictive-maintenance purpose.

The INFN-CNAF computing center, one of the Worldwide LHC Computing Grid Tier-1 sites, is serving a large set of scientific communities, in High Energy Physics and beyond. In order to increase efficiency and to remain competitive in the long run, CNAF is launching various activities aiming at implementing a global predictive maintenance solution for the site. This requires a site-wide effort in collecting, cleaning and structuring all possibly useful data coming from log files of the various Tier-1 services and systems, as a necessary step prior to designing machine learning based approaches for predictive maintenance. Among the Tier-1 services, efficient storage systems are one of the key ingredients of Tier-1 operations. CNAF uses the StoRM service as a Grid Storage Resource Manager solution: its operations are logged in a very complex manner, as the log content is deeply unstructured and hard to be exploited for analytics purposes. Despite such difficulty, the StoRM logs are a precious source of information for operators (e. g. real-time monitoring and anomaly detection), for developers (e. g. debugging, service stability, code improvements) and for site managers (service optimization, storage usage efficiency, time and money saving ways to spot and prevent unwanted behaviors). Based on previous experiences on Big Data Analytics and Machine/Deep learning in the CMS experiment, this work describes how the StoRM logs can be handled and parsed to extract the relevant information, how such log handling can be designed to work automatically, how to define and implement metrics to tag critical states of the service, how to correlate StoRM events with external services events, and ultimately how to contribute to the future CNAF-wide predictive maintenance system. Initial results in this activity are presented and discussed. Furthermore, a mention to ongoing complementary work at the CNAF center is also mentioned.

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