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The carbohydrate-deficient glycoprotein syndrome (CDGS) is a developmental disease associated with an abnormally high isoelectric point of serum transferrin. Carbohydrate analyses of this glycoprotein initially suggested a defect in N-linked oligosaccharide processing, although more recent studies indicate a defect in the attachment of these sugar chains to the protein. We studied both serum glycoproteins and fibroblast-derived [2-3H]mannose-labeled oligosaccharides from CDGS patients and normal controls. While there was a decrease in the glycosylation of serum glycoproteins of affected individuals, differences were not seen in either monosaccharide composition or oligosaccharide structures. The lectin-binding profiles of glycopeptides from [2-3H]-mannose-labeled fibroblasts were likewise indistinguishable. However, the incorporation of [2-3H]mannose into both glycoproteins and the dolichol-linked oligosaccharide precursor was significantly reduced. Thus, at least in some patients, CDGS is not due to a defect in processing of N-linked oligosaccharides, but rather to defective synthesis and transfer of nascent dolichol-linked oligosaccharide precursors. This abnormality could result in both a failure to glycosylate some sites on some proteins, as well as secondary abnormalities in overall glycoprotein processing and/or function. (J.

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Education 4.0 imitates Industry 4.0 in many aspects such as technology, customs, challenges, and benefits. The remarkable advancement in embryonic technologies, including IoT (Internet of Things), Fog Computing, Cloud Computing, and Augmented and Virtual Reality (AR/VR), polishes every dimension of Industry 4.0. The constructive impacts of Industry 4.0 are also replicated in Education 4.0. Real-time assessment, irregularity detection, and alert generation are some of the leading necessities of Education 4.0. Conspicuously, this study proposes a reliable assessment, irregularity detection, and alert generation framework for Education 4.0. The proposed framework correspondingly addresses the comparable issues of Industry 4.0. The proposed study (1) recommends the use of IoT, Fog, and Cloud Computing, i.e., IFC technological integration for the implementation of Education 4.0. Subsequently, (2) the Symbolic Aggregation Approximation (SAX), Kalman Filter, and Learning Bayesian Network (LBN) are deployed for data pre-processing and classification. Further, (3) the assessment, irregularity detection, and alert generation are accomplished over SoTL (the set of threshold limits) and the Multi-Layered Bi-Directional Long Short-Term Memory (M-Bi-LSTM)-based predictive model. To substantiate the proposed framework, experimental simulations are implemented. The experimental outcomes substantiate the better performance of the proposed framework, in contrast to the other contemporary technologies deployed for the enactment of Education 4.0.