Entrepreneurial education and its role in fostering sustainable communities

Establishing sustainable communities requires bridging the gap between academic knowledge and societal requirements; this is where entrepreneurial education comes in. The first phase involved a comprehensive review of the literature and extensive consultation with experts to identify and shortlist the components of entrepreneurship education that support sustainable communities. The second phase involved Total Interpretative Structural Modelling to explore or ascertain how the elements interacted between sustainable communities and entrepreneurial education. The factors are ranked and categorized using the Matrice d'impacts croises multiplication appliquee an un classement (MICMAC) approach. The MICMAC analysis classifies partnerships and incubators as critical drivers, identifying Student Entrepreneurship Clubs and Sustainability Research Centers as dependent elements. The study emphasizes alumni networks and curriculum designs as key motivators. The results highlight the critical role that well-designed entrepreneurial education plays in developing socially conscious entrepreneurs, strengthening communities, and generating long-term job prospects. The study provides a valuable road map for stakeholders dedicated to long-term community development agendas by informing the creation of strategic initiatives, curriculum updates, and policies incorporating entrepreneurial education.

An Energy-Efficient and Blockchain-Integrated Software Defined Network for the Industrial Internet of Things

The number of unsecured and portable Internet of Things (IoT) devices in the smart industry is growing exponentially. A diversity of centralized and distributed platforms have been implemented to defend against security attacks; however, these platforms are insecure because of their low storage capacities, high power utilization, single node failure, underutilized resources, and high end-to-end delay. Blockchain and Software-Defined Networking (SDN) are growing technologies to create a secure system and to ensure safe network connectivity. Blockchain technology offers a strong and trustworthy foundation to deal with threats and problems, including safety, privacy, adaptability, scalability, and security. However, the integration of blockchain with SDN is still in the implementation phase, which provides an efficient resource allocation and reduced latency that can overcome the issues of industrial IoT networks. We propose an energy-efficient blockchain-integrated software-defined networking architecture for Industrial IoT (IIoT) to overcome these challenges. We present a framework for implementing decentralized blockchain integrated with SDN for IIoT applications to achieve efficient energy utilization and cluster-head selection. Additionally, the blockchain-enabled distributed ledger ensures data consistency throughout the SDN controller network and keeps a record of the nodes enforced in the controller. The simulation result shows that the proposed model provides the best energy consumption, end-to-end latency, and overall throughput compared to the existing works.

Artificial Bee Colony Reinforced Extended Kalman Filter Localization Algorithm in Internet of Things with Big Data Blending Technique for Finding the Accurate Position of Reference Nodes

In recent years, the growth of internet of things (IoT) is immense, and the observations of their evolution need to be carried out effectively. The development of the IoT has been broadly adopted in the construction of intelligent environments. There are various challenging IoT issues such as routing messages, addressing, Localizing nodes, data blending, etc. Formerly learning eloquent information from big data systems to construct a data-gathering setup in an IoT environment is challenging. Among many viable data sources, the IoT is a rich big data source: Various IoT nodes produce a massive quantity of data. Localization is one of the crucial problems that make a significant impact inside the IoT system. It needs to be engaged with proper and effective procedures to collect all sorts of data without noise. Numerous localization procedures and schemes have been initiated by deploying the IoT sensor with wireless sensor networks for both interior and outside environments. To accomplish higher localization accuracy, with less cost for the large volume of data, it is considered a hectic task in the IoT sensor environment. By viewing the nature of the IoT, the merging of different technologies such as the internet, WiFi, etc., can aid diverse ways to acquire information about various objects' locations. Location-based service is an exceptional service of the IoT, whereas localization accuracy is a significant issue. The data generated from the sensor are available in both static and dynamic forms. In this article, a sophisticated accuracy localization scheme for big data is proposed with an optimization approach that can effectively produce proper and effective outcomes for IoT environments. The theme of the article is to develop an enriched Swarm Intelligence algorithm based on Artificial Bee Colony by using the EKF (Extended Kalman Filter) data blend technique for improving Localization in IoT for the unsuspecting environment. The performance of the proposed algorithm is evaluated by using communication consumption and Localization accuracy and its comparative advantage.

Heart disease prediction using stacked ensemble technique

Heart diseases are one of the crucial diseases that may cause fatality in both men and women. About 12 million deaths occur across the world due to heart diseases. With the advancement in information technology, it is possible for the Healthcare industry to store enormous volume of data containing millions of patient’s medical information along with their treatment details. If utilized in an efficient manner, this information helps the doctors to diagnose the diseases in a precise manner. Data mining algorithms are employed to analyse huge data sets and to discover unseen patterns. Data mining plays an essential role in medical diagnosis. Doctors bank on different computer models which uses data mining algorithms to prefigure different kinds of diseases in patients. So, the need is to design a methodical data mining algorithm that helps for better forecast of diseases. The main goal of this work is to create an ensemble of algorithms which results in better accuracy. The ensemble is constructed by making use of stacking ensemble technique, which comprises of two categorization algorithms namely Naïve Bayes and Artificial Neural Network. The Cleveland heart disease data set acquired from UCI machine learning repository containing 14 attributes and 303 instances is given as input to these algorithms. From our experimental analysis it is evident that the proposed ensemble scheme results in a better accuracy.

A fuzzy based high-resolution multi-view deep CNN for breast cancer diagnosis through SVM classifier on visual analysis

Breast cancer should be diagnosed as early as possible. A new approach of the diagnosis using deep learning for breast cancer and the particular process using segmentation strategies presented in this article. Medical imagery is an essential tool used for both diagnosis and treatment in many fields of medical applications. But, it takes specially trained medical specialists to read medical images and make diagnoses or treatment decisions. New practices of interpreting medical images are labour exhaustive, time-wasting, expensive, and prone to error. Using a computer-aided program which can render diagnosis and treatment decisions automatically would be more beneficial. A new computer-based detection method for the classification between compassionate and malignant mass tumours in mammography images of the breast proposed. (a) We planned to determine how to use the challenging definition, which produces severe examples that boost the segmentation of mammograms. (b) Employing well designing multi-instance learning through deep learning, we validated employing inadequately labelled data of breast cancer diagnosis using a mammogram. (c) The study is going through the Deep Lung method incorporating deep multi-dimensional automated identification and classification of the lung nodule. (d) By combining a probabilistic graphic model in deep learning, it authorizes how weakly labelled data can be used to improve the existing breast cancer identification method. This automated system involves manually defining the Region Of Interest (ROI), with the region and threshold values based on the next region. The High-Resolution Multi-View Deep Convolutional Neural Network (HRMP-DCNN) mainly developed for the extraction of function. The findings collected through the subsequent in available public databases like mammography screening information database and DDSM Curated Breast Imaging Subset. Ultimately, we’ll show the VGG that’s thousands of times quicker, and it is more reliable than earlier programmed anatomy segmentation.

Images super-resolution by optimal deep AlexNet architecture for medical application: A novel DOCALN1

In the last decade, numerous researches have been focused on Image Super-Resolution (SR); this recreation or improvement model is vital in different research areas. Recently, deep learning algorithm finds useful to advance in the resolution of the medical output. Here, we devise a novel Deep Convolutional Network model along with the optimal learning rate of the Rectified Linear Unit (ReLU) intended for Medical Image Super-Resolution (MISR). For getting the optimal values of Deep Learning AlexNet structure, Modified Crow Search (MCS) is utilized, which is mainly depends on the behavior of crow sets. The chosen Alexnet lacks in a sort of suitable supervision for upgrading execution of the proposed model that effectively aims to overfit. The proposed design, i.e., MISR, named Deep Optimal Convolutional AlexNet (DOCALN), derives the optimal values of learning rates of the ReLU activation function. Based on this optimal deep learning structure, the Low Resolution (LR) medical images can be applied. Experimentation results of our proposed model are compared with variants of Convolution Neural Networks (CNN) concerning different measures such as image quality assessment, SR efficiency analysis, and execution time.

Ensemble Gaussian mixture model-based special voice command cognitive computing intelligent system

Dysarthria is a speech disorder caused by stroke, Parkinson’s disease, neurological injury, or tumors that damage the nervous system and weaken the speech quality. Developing a unique voice command system for Dysarthric speech helps to recognize impaired speech and convert them into text or input commands. Hidden Markov Model (HMM) is one of the widely used generative model-based classifiers for Dysarthric speech recognition. But due to insufficient training data, HMM doesn’t provide optimal results on overlapping classes. We propose an ensemble Gaussian mixture model to recognize impaired speech more accurately. Our model converts the sequence of feature vectors into a fixed dimensional representation of patterns with varying lengths. The performance efficiency of the proposed model is evaluated on the Dysarthric UA-speech benchmark dataset. The discriminatory information provided by the proposed approach yields better classification accuracy even for shallow intelligibility words compared to conventional HMM.

Fuzzy based optimal switching angle-PWM controller for 27-level asymmetric multi-level inverter

This work aims to improve the operation of multi-level inverter with reduced switching losses, thereby propose a new structure for an MLI with a reduced component count. A 27-level asymmetric Multi Level Inverter (MLI) with a minimal number of static switches is considered as a test system. The proposed MLI is developed with three input DC sources and thirteen power electronic switches. The hardware prototype is developed for 40 V and 3.5 A output. The control logic is developed in dsPIC30F410 controller. The main objective of this work is to effectively bring down the Total Harmonic Distortion (THD) of the resulting output voltage by analyzing the harmonic spectrum of the proposed MLI configuration with various low frequencies switching techniques and optimizing their switching angles and to choose an appropriate switching state using fuzzy logic controller (FLC). The proposed FLC covers wide range of operating conditions i.e. 10 switching states and variables 9*9 rules to predict the suitable switching angel. The performance metrics of the proposed structure of 27-level MLI has been evaluated upon simulation results and experimental results based on hardware prototype. The comparative study also carried out with the recent MLI topologies.

White blood cell detection and classification using Euler’s Jenks optimized multinomial logistic neural networks

Due to the wide acceptance of White Blood Cells (WBCs) in disease diagnosis, detection and classification of WBC are hot topic. Existing methodologies have some drawbacks such as significant degree of error, higher accuracy, time bound and higher misclassification rate. A WBCs detection and classification called, Jenks Optimized Logistic Convolutional Neural Network (JO-LCNN) method has proposed. Initally, Eulers Principal Axis is used as a convolution model to obtain a rotation invariant form of image by differentiating the background and RBCs, then eliminating them which leaves only the WBCs. By eliminating the wanton features, inherent features are detected contributing to minimum misclassification rate. According to above, Jenks Optimization function is used as a pooling model to obtain feature map for lower resolution. Therefore JO-LCNN is used for removing tiny objects in image and complete nuclei. Finally, Multinomial Logistic classifier is used to classify five types of classes by means of loss function and updating weight according to the loss function, therefore classifying with higher accuracy rate. Using LISC database for WBCs with different parameters as classification accuracy, false positive rate and time complexity are performed. Result shows that JO-LCNN, efficiently improves accuracy with less time, misclassification rate than the state-of-art methods.

Requirement of ErbB2 for signalling by interleukin-6 in prostate carcinoma cells

Interleukin-6 (IL-6) is a cytokine that was initially recognized as a regulator of immune and inflammatory responses, but it also regulates the growth of many tumour cells, including prostrate carcinoma. Overexpression of the growth-factor receptors ErbB2/neu and ErbB3 has been implicated in the neoplastic transformation of prostate carcinoma. Here we show that treatment of the prostate cancer cell line LNCaP with IL-6 induces tyrosine phosphorylation of ErbB2 and ErbB3, but not ErbB1/EGFR. We also show that ErbB2 forms a complex with the gp130 subunit of the IL-6 receptor in an IL-6-dependent manner. This association is important because the inhibition of ErbB2 activity results in abrogation of IL-6-induced MAPK activation. Thus ErbB2 is a critical component of IL-6 signalling through the MAP kinase pathway. These data show how a cytokine receptor can diversify its signalling pathways by engaging with a growth-factor receptor kinase.

A minor tyrosine phosphorylation site located within the CAIN domain plays a critical role in regulating tissue-specific transformation by erbB kinase

Avian c-erbB encodes a protein that is homologous to the human epidermal growth factor receptor. Truncation of the amino-terminal, ligand-binding domain of this receptor results in an oncogene product which is a potent inducing agent for erythroleukemias but not fibrosarcomas in chickens. Here we show that mutation of a single tyrosine residue, p5, in the carboxyl terminus of the erbB oncogene product allows it to become sarcomagenic in vivo and to transform fibroblasts in vitro. Mutations of other autophosphorylation sites do not generate comparable effects. The increased transforming activity of the p5 mutant is accompanied by an elevated level of mitogen-activated protein kinase phosphorylation. By analogy to the human epidermal growth factor receptor, p5 is a minor autophosphorylation site and is located in a domain known to be involved in regulating calcium influx and receptor internalization (CAIN domain). This area of the erbB product has been found to be repeatedly deleted in various sarcomagenic avian erythroblastosis virus isolates. We precisely deleted the CAIN domain and also made point mutations of the acidic residues within the CAIN domain. In both cases, fibroblast-transforming potential is activated. We interpret these data to mean that p5 and its surrounding region negatively regulate fibroblast-transforming and sarcomagenic potential. To our knowledge, this represents the first point mutation of an autophosphorylation site that activates erbB oncogenicity.

Modulation of erbB kinase activity and oncogenic potential by single point mutations in the glycine loop of the catalytic domain

Avian c-erbB is activated to a leukemia oncogene following truncation of its amino-terminal ligand-binding domain by retroviral insertion. The insertionally activated transcripts encode protein products which have constitutive tyrosine kinase activity and can induce erythroleukemia but not sarcomas. We have previously found that a valine-to-isoleucine point mutation at position 157 (V157I mutant) within the tyrosine kinase domain of this truncated erbB can dramatically activate the sarcomagenic potential of the oncogene and increase the kinase activity of this oncoprotein. This mutation lies at position 157 of the insertionally activated c-erbB product, affecting a highly conserved valine residue of the glycine loop involved in ATP binding and phosphate transfer. To investigate the functional importance of this residue in the catalytic activity of kinases, we have introduced at this position, by site-directed mutagenesis, codons representing the remaining 18 amino acid residues. Most of the mutants have diminished activity, with six of them completely devoid of kinase activity, indicating the sensitivity of this region to conformational changes. Some of these mutants displayed increased kinase activity and greater transforming potential in comparison with IA c-erbB, but none had levels as high as those of the V157I mutant. In general, the sarcomagenic potential of the various erbB mutants correlated with their autophosphorylation state and their ability to cause phosphorylation of MAP kinase. However, there are important exceptions such as the V157G mutant, which lacks enhanced autophosphorylation but is highly sarcomagenic. Studies of this and other autophosphorylation site mutants point to the existence of an autophosphorylation-independent pathway in sarcomagenesis. The requirement for leukemogenic potential is much less stringent and correlates with positivity of kinase activity. When the valine-to-isoleucine substitution was put in context of the full-length erbB protein, the mutation relaxed the ligand dependence and had a positive effect on the transforming potential of the full-length c-erbB.

Glycoinositol phospholipid anchor-defective K562 mutants with biochemical lesions distinct from those in Thy-1- murine lymphoma mutants

Deficient expression of glycoinositol phospholipid (GPI)-anchored surface proteins has been linked to six different genetic defects in Thy-1- murine lymphoma mutants. In this study, human K562 cell mutants defective in GPI anchoring were derived by anti-decay-accelerating factor (CD55) based negative fluorescent cell sorting of N-methyl-N'-nitro-N-nitrosoguanidine pretreated cells. Homologous cell fusions of six clones that complemented a previously described K562 mutant corresponding to one of the Thy-1- mutant classes (Hirose, S., Mohney, R. P., Mutka, S. C., Ravi, L., Singleton, D. R., Perry, G., Tartakoff, A., and Medof, M. E. (1992) J. Biol. Chem. 267, 5272-5278) showed that they segregated into two complementation groups. In heterologous cell fusions, representative clones of each group complemented Thy-1 expression by all of the previously described GPI anchor pathway-defective Thy-1- murine lymphoma classes (A, B, C, E, F, and H) but not class(es) D (and I) defective in the Thy-1 structural gene. Analyses of putative GPI anchor precursors synthesized by the two lines revealed that one mutant exhibited a complete block in deacetylation of N-acetyl-D-glucosamine-inositol phospholipid to glucosamine (GlcN)-inositol phospholipid, whereas the other mutant assembled GlcN-inositol phospholipid and subsequent mannose (Man)-containing intermediates but showed markedly increased amounts of the terminal ethanolamine (EthN)-phosphate (P)-substituted putative anchor precursors, EthN-P-6ManMan(EthN-P-->)ManGlcN- and EthN-P-6Man(EthN-P-6)Man(EthN- P-->)ManGlcN-acylinositol phospholipid (H7 and H8). We designate these new complementation classes J, harboring a defect in N-acetyl-D-glucosamine-inositol phospholipid deacetylation, and K, deficient in a step preliminary to or associated with protein transfer of assembled anchor precursors. The availability of these new mutant classes should aid in characterization of the GPI anchor pathway enzymes providing for these reactions.

Characterization of putative glycoinositol phospholipid anchor precursors in mammalian cells. Localization of phosphoethanolamine

A number of mammalian cell surface proteins are anchored by glycoinositol phospholipid (GPI) structures that are preassembled and transferred to them in the endoplasmic reticulum. The GPIs in these proteins contain linear ethanolamine (EthN)-phosphate (P)-6ManManManGlcN core glycan sequences bearing an additional EthN-P attached to the Man residue (Man 1) proximal to GlcN. The biochemical precursors of mammalian GPI anchor structures are incompletely characterized. In this study, putative [3H]Man-labeled GPI precursors were obtained by in vitro GDP-[3H] Man labeling of HeLa cell microsomes and by in vivo [3H]Man labeling of class B and F Thy-1 negative murine lymphoma mutants known to accumulate incomplete GPIs. The high performance liquid chromatography-purified in vitro and accumulated in vivo GPI products were structurally analyzed by nitrous acid deamination, hydrofluoric acid, trifluoroacetic acid hydrolysis, biosynthetic labeling, and exoglycosidase treatment. The data were consistent with a biosynthetic scheme in which Man and EthN-P are added stepwise to the developing glycan. Several additional points were demonstrated: 1) putative mammalian GPI precursors contain incomplete core glycans corresponding to those in previously characterized trypanosome GPI precursors. 2) The proximal EthN-P found in mature mammalian GPI anchor structures is added to Man 1 prior to incorporation of Man 2 and Man 3. 3) Glycans in the incomplete GPIs that accumulate in classes B and F lymphoma mutants consist of Man2- and Man3GlcN in which EthN-P is linked to Man 1. 4) Distal EthN-P linked to the 6-position of Man, characteristic of the complete GPI core, is found both in a subsequent GPI species with the glycan sequence EthN-P-6ManMan(EthN-P----)ManGlcN and in a more polar GPI product.

Synthesis of mannosylglucosaminylinositol phospholipids in normal but not paroxysmal nocturnal hemoglobinuria cells

To identify mannosyl (Man)-containing intermediates of the human glycoinositol phospholipid (GPI) anchor pathway and examine their expression in paroxysmal nocturnal hemoglobinuria (PNH), mannolipid products deriving from in vitro guanosine diphosphate [3H]Man labeling of HeLa cell microsomes were characterized. The defined GPI species were correlated with products deriving from in vivo [3H]Man labeling of normal and (GPI-anchor defective) affected leukocytes. In vitro analyses in HeLa cells showed dolichol-phosphoryl (Dol-P)-[3H]Man and a spectrum of [3H]Man lipids exhibiting TLC mobilities approximating those of Trypanosoma brucei (Tryp) GPI precursors. Iatrobead HPLC separations and partial characterizations of the major isolated [3H]Man species (designated H1-H8) showed that all but H1 (Dol-P-Man) were sensitive to HNO2 deamination and serum GPI-specific phospholipase D digestion but were resistant to phosphatidylinositol-specific phospholipase C digestion unless previously deacylated with mild alkali. [3H]Man label in H3, H4, and H6 but not in H5 or H7 was efficiently released into the aqueous phase by jack bean alpha-mannosidase digestion. BioGel P-4 and AX-5 sizing of the dephosphorylated core glycan fragments of H6 and H7 gave values that coincided precisely with the corresponding glycan fragments from the fully assembled Tryp anchor donor A' (P2). Affected leukocytes from four patients with PNH supported formation of GlcNAc- and GlcN-PI but all failed to express H6 and H7 as well as H8 and two showed complete absence of earlier Man-containing intermediates. These findings argue that human intracellular GPI mannolipids are built on acylated inositol phospholipids, that H6 and H7 contain differentially phosphoethanolamine-substituted Man3-GlcN-inositol cores, and that PNH cells are defective in conversion of GlcN-PI into these more mature mannolipid structures.

Derivation and characterization of glycoinositol-phospholipid anchor-defective human K562 cell clones

To aid in studies of human glycoinositol-phospholipid (GPI) anchor pathway biochemistry in normal and affected paroxysmal nocturnal hemoglobinuria cells, GPI anchor-defective human K562 cell lines were derived by negative fluorescent sorting of anti-decay-accelerating factor (DAF) monoclonal antibody-stained cells either following or in the absence of ethylmethylsulfonate pretreatment. The resulting cloned cells showed deficiencies of both DAF and GPI-anchored CD59, some (designated group A) exhibiting total absence and some (designated group B) exhibiting approximately 10% levels of surface expression of the two proteins. In heterologous cell fusions, group A clones complemented defective Thy-1 expression by class A, B, C, E, and I Thy-1-negative lymphoma lines, but not H or D lines, the latter of which is defective in the Thy-1 structural gene. In contrast, group B clones complemented all previously described GPI anchor pathway-defective lymphoma classes. Immunoradiomatic assays of cells and supernatants and 35S biosynthetic labeling showed that group A cells degraded DAF protein while group B cells secreted it but failed to attach a GPI anchor structure. [3H]Man labeling of intact cells and UDP-[3H]GlcNAc and GDP-[3H]Man labeling of broken cell preparations demonstrated that group A cells failed to synthesize GlcNAc- and GlcN-PI (GPI-A and -B) as well as more polar mannolipids, whereas group B cells showed accumulation of GlcNAc-PI with approximately 10-fold diminished levels of GlcN-PI and more polar mannolipids. The failed assembly of GlcNAc-PI in group A cells and the reduced conversion of this intermediate to GlcN-PI in group B cells indicates that the former harbors a defect in UDP-GlcNAc transferase or in assembly of its PI acceptor, while the latter harbors a defect in GlcN-PI deacetylase activity.

Characterization of the decay-accelerating factor gene promoter region

Decay-accelerating factor (DAF) expression modulates susceptibility of cells to autologous complement attack. To characterize the regulatory region controlling DAF gene transcription, genomic DNA extending from 815 base pairs (bp) upstream to approximately 4 kilobases downstream of DAF's AUG codon (designated +1) was cloned and sequenced. The 5' flanking sequence showed 59-76% G + C content (-355 to +1), at least one GC box(es) (-135 to -131), and variable length sequences (from -629 to -285) conforming to the motifs TCCTCC and TCn. Nuclease S1 digestions and primer extensions localized a major transcriptional start site to -82/-81, 38 bp downstream of a possible TATA variant, (A)TTTAA. In COS cell transfections, the sequence encompassing -815 to -67 functioned 2.5% as efficiently as the Rous sarcoma virus 3' long terminal repeat, but following deletion upstream of -355 its activity increased approximately 4-fold. Two octanucleotides exhibiting partial homology to phorbol 12-myristate 13-acetate (PMA) and cAMP responsive elements (PREs and CREs, respectively) were detected, and the respective modulators enhanced transcriptional efficiency 2- and approximately 10-fold, respectively. Thus, the DAF gene promoter (i) exhibits sequences resembling both conventional and unconventional transcriptional control elements, (ii) possesses a region with negative regulatory activity, and (iii) responds to PMA and cAMP induction presumably via PRE- and CRE-like enhancer elements.

Assembly and deacetylation of N-acetylglucosaminyl-plasmanylinositol in normal and affected paroxysmal nocturnal hemoglobinuria cells

Decay-accelerating factor (DAF) is anchored in cell membranes by a glycosyl-plasmanylinositol (GPI) moiety that is transferred to it en bloc in the rough endoplasmic reticulum. To analyze the biochemical reactions involved in preassembly of this structure, a human hematopoietic cell-free system was employed. Incubation of cell extracts with UDP-[3H]GlcNAc and butanol partitioning of reaction mixtures yielded two products similar in TLC mobility to intermediates described in Trypanosoma brucei. Both species were sensitive to Bacillus thuringiensis phosphatidylinositol-specific phospholipase C, indicative of association of [3H]GlcNAc label with a plasmanylinositol-containing acceptor. In contrast to trypanosome intermediates, which contain phosphatidylinositol (1,2-diacylglycerophosphoinositol), however, alkali treatment and phospholipase A2 digestion generated butanol-phase products characteristic of glycosylated plasmanylinositol (1-alkyl-2-acylglycerophosphoinositol). Kinetic and pulse-chase experiments indicated that the slower-migrating species was a product of the faster and that it, but not the faster, was sensitive to both GPI-specific phospholipase D and nitrous acid deamination, consistent with conversion of GlcNAc- to GlcN-plasmanylinositol. Accordingly, acetic anhydride acetylation retransformed the slower species back to the faster. Further incubation with cell extracts converted the slower species into more polar products. Lysates of normal and of affected blood leukocytes from two paroxysmal nocturnal hemoglobinuria (PNH) patients supported assembly of the two intermediates within 1 min. Thus, the initial enzymes mediating human GPI-anchor assembly are GlcNAc-plasmanylinositol transferase and GlcNAc-plasmanylinositol deacetylase, their substrates contain plasmanylinositols, and the products of their activities are normal in affected PNH cells.