| 1. |
- Carroll, Daniela J, et al.
(författare)
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Interleukin-22 regulates B3GNT7 expression to induce fucosylation of glycoproteins in intestinal epithelial cells.
- 2021
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Ingår i: The Journal of biological chemistry. - : Elsevier BV. - 1083-351X .- 0021-9258. ; 298:2
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Tidskriftsartikel (refereegranskat)abstract
- Interleukin (IL)-22 is a cytokine that plays a critical role in intestinal epithelial homeostasis. Its downstream functions are mediated through interaction with the heterodimeric IL-22 receptor and subsequent activation of signal transducer and activator of transcription 3 (STAT3). IL-22 signaling can induce transcription of genes necessary for intestinal epithelial cell proliferation, tissue regeneration, tight junction fortification, and antimicrobial production. Recent studies have also implicated IL-22 signaling in the regulation of intestinal epithelial fucosylation in mice. However, whether IL-22 regulates intestinal fucosylation in human intestinal epithelial cells and the molecular mechanisms that govern this process are unknown. Here, in experiments performed in human cell lines and human-derived enteroids, we show that IL-22 signaling regulates expression of the B3GNT7 transcript, which encodes a β1-3-N-acetylglucosaminyltransferase that can participate in the synthesis of poly-N-acetyllactosamine (polyLacNAc) chains. Additionally, we find that IL-22 signaling regulates levels of the α1-3-fucosylated Lewis X (Lex) blood group antigen, and that this glycan epitope is primarily displayed on O-glycosylated intestinal epithelial glycoproteins. Moreover, we show that increased expression of B3GNT7 alone is sufficient to promote increased display of Lex-decorated carbohydrate glycan structures primarily on O-glycosylated intestinal epithelial glycoproteins. Together, these data identify B3GNT7 as an intermediary in IL-22-dependent induction of fucosylation of glycoproteins and uncover a novel role for B3GNT7 in intestinal glycosylation.
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| 2. |
- Khan, Mohd Ziyauddin, et al.
(författare)
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Modeling enablers of agile and sustainable sourcing networks in a supply chain: A case of the plastic industry
- 2024
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Ingår i: Journal of Cleaner Production. - : ELSEVIER SCI LTD. - 0959-6526 .- 1879-1786. ; 435
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Tidskriftsartikel (refereegranskat)abstract
- To compete in emerging markets, a supply chain must perform well. Agile and sustainable sourcing practices can improve supply chain performance; however, their impact needs an optimal evaluation. Although few research studies offer frameworks for integrating agile and sustainable principles, none offer links to implementing these practices in the sourcing networks of a supply chain. The present study seeks to bridge these gaps by developing a framework that identifies and configures the enabling elements for creating agile and sustainable sourcing networks. This study aims to provide an implementable causal model that the plastic industry's supply chain could adopt. In the first phase of the research process, fifteen enablers are identified through literature and validated by Delphi experts. In the second phase, interpretive structural modeling is applied to establish the hierarchical relationships among these enablers and categorize them based on their functionalities. The model is demonstrated based on the real-life case study of a firm manufacturing plastic pipes and fittings. The proposed model identifies the strategic, operational, and performance level enablers and intends to help the managers incorporate the agile and sustainable criteria in their sourcing practices. The findings of this study provide several contributions to the literature and implications for the plastic industry.
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| 3. |
- Kumar, Ashwani, et al.
(författare)
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Exploring Filter Banks and Spike Interval Statistics of Level-Crossing ADCs for Fault Diagnosis of Rolling Element Bearings
- 2023
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Ingår i: Proceedings of the Annual Conference of the PHM Society 2023. - : PHM Society.
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Konferensbidrag (refereegranskat)abstract
- Nowadays, lots of data are generated in industries using vibration sensors to evaluate the equipment’s working condition and identify faults. A significant challenge is that only a small fraction of data can be transmitted for intelligent fault diagnosis and storage. The edge processing capacity is often insufficient for advanced analysis due to time and resource constraints. The neuromorphic signal encoding scheme efficiently reduces the data rate by encoding relevant signal changes into spike trains while discarding redundant information and noise, enabling energy-efficient neuromorphic processing. Due to the presence of dominant operational features and noise in the original measurements, signal pre-processing is required to extract the relevant features before spike coding and processing. The work investigates the effects of different filter banks (pre-processing methods) on the spike encodings for vibration measurements from bearings. This also includes bearing fault features diagnosis based on statistical analysis of generated spikes. The comparative analysis is made for benchmarking different signal pre-processing methods (e.g., envelope, empirical mode decomposition (EMD), and gammatone filter) on bearing vibration datasets. An event-triggered scheme, i.e., Level-crossing analog-to-digital converters (LC-ADCs) is applied to encode the vibration measurement to spikes. Inter-spike intervals (ISIs) statistics are analysed for fault diagnosis of bearings. The results obtained for CWRU bearing databases indicate a possible fault detection and diagnosis with significant data rate reduction and an opportunity for improved computational efficiency. With the developed approach, the envelope filter is found to be the most efficient of all. This work enables a new approach to improve the energy efficiency of condition monitoring systems and further sets a new course of research development in this area using neuromorphic technologies.
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| 4. |
- Kumar, Ashwani, et al.
(författare)
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Sustainable waste electrical and electronic equipment management guide in emerging economies context : A structural model approach
- 2022
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Ingår i: Journal of Cleaner Production. - : Elsevier Science Ltd. - 0959-6526 .- 1879-1786. ; 336
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Tidskriftsartikel (refereegranskat)abstract
- With globalization and the rapid advancement of information technology, waste electrical and electronic equipment (WEEE) management has become a significant concern among electronic manufacturers. It motivated researchers to identify barriers and enablers of sustainable WEEE management. However, existing literature could not capture multi-stakeholders perspective while identifying enablers crucial for developing sustainable WEEE management policy, especially in emerging economies. The present study fulfils the gap by considering multi-stakeholders perspective to identify enablers of sustainable WEEE management in an emerging economy, i.e., India. We identified 23 potential enablers through literature review and discussion with domain experts. Subsequently, the finalized enablers were analyzed to uncover the cause-effect relationship using a hybrid grey based decision-making trial and evaluation laboratory (DEMATEL) approach. Findings revealed that research and development capabilities and digitization, extended producer responsibility, monitoring of illegal import and dumping, environmental regulations and WEEE policies, and use of green or cleaner technologies for waste recycling were recognized as the most significant causal enablers. The study contributes to the theoretical knowledge by categorizing enablers under different theoretical frameworks. It can also assist policymakers, practitioners, and electronic manufacturers in framing policies related to the circular economy and sustainable WEEE management to meet the sustainable development goals of 2030.
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| 5. |
- Mojumder, Abhishek, et al.
(författare)
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Mitigating the barriers to green procurement adoption : An exploratory study of the Indian construction industry
- 2022
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Ingår i: Journal of Cleaner Production. - : Elsevier Science Ltd. - 0959-6526 .- 1879-1786. ; 372
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Tidskriftsartikel (refereegranskat)abstract
- The construction sector in India is in the governments intense focus on creating a world-class infrastructure balancing environmental conservation. Adopting green procurement practices by mitigating adoption barriers is necessary for Indian construction firms to achieve the goal. However, none of the existing research identified the comprehensive list of barriers, analysed their impacts on green procurement adoption, prioritized the barriers and formulated the solution strategies to mitigate them and maximize green procurement adoption for the Indian construction sector. To bridge this gap, this study has identified barriers, analysed their impact, prioritized the criticality, and developed the solution strategies to alleviate them. Questionnaire surveys and descriptive sta-tistics are first performed for data analysis of the firms based on the firms size and domain of expertise. Later, an analysis of variance (ANOVA) is performed and identified the significant differences in the impact of the barriers on Indian construction firms having different sizes or domains of expertise. The fuzzy best-worst method (FBWM) is then used to identify the most significant barriers as "reduced commitment from higher management ", "lack of management support ", and "perception of higher cost for adhering to green procurement ". Finally, the Delphi technique and assessment of various portals of the Government of India (GOI) have been carried out to identify the solutions to mitigate the barriers. The research results in an original and unique approach to identifying and analysing the critical barriers to green procurement adoption and their impact on different categories of Indian construction firms. It has identified the topmost barriers and then the solution strategies Indian construction firms and GOI need to focus on to embrace green procurement. The procurement managers can identify the top -rated barriers derived from the present study to closely focus and make strategies to eliminate them, helping their organisations adopt green procurement practices. The solution strategies derived from the study may be ready to implement action plans for construction and infrastructure companies of India, environmental and social development of the country, and assisting the GOI in developing and implementing the policy of green pro-curement for the Indian construction industry.
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| 6. |
- Shrivastava, Nidhi, et al.
(författare)
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Important amino acid residues of hexachlorocyclohexane dehydrochlorinases (LinA) for enantioselective transformation of hexachlorocyclohexane isomers
- 2017
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Ingår i: Biodegradation. - : SPRINGER. - 0923-9820 .- 1572-9729. ; 28:2-3, s. 171-180
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Tidskriftsartikel (refereegranskat)abstract
- LinA-type1 and LinA-type2 are two well-characterized variants of the enzyme hexachlorocyclohexane (HCH)-dehydrochlorinase. They differ from each other at ten amino acid positions and exhibit differing enantioselectivity for the transformation of the (-) and (+) enantiomers of alpha-HCH. Amino acids responsible for this enantioselectivity, however, are not known. An in silico docking analysis identified four amino acids (K20, L96, A131, and T133) in LinA-type1 that could be involved in selective binding of the substrates. Experimental studies with constructed mutant enzymes revealed that a combined presence of three amino acid changes in LinA-type1, i.e. K20Q, L96C, and A131G, caused a reversal in its preference from the (-) to the (+) enantiomer of alpha-HCH. This preference was enhanced by the additional amino acid change T133 M. Presence of these four changes also caused the reversal of enantioselectivity of LinA-type1 for delta-HCH, and beta-, gamma-, and delta-pentachlorocyclohexens. Thus, the residues K20, L96, A131, and T133 in LinA-type1 and the residues Q20, C96, G131, and M133 in LinA-type 2 appear to be important determinants for the enantioselectivity of LinA enzymes.
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| 7. |
- Strömbergsson, Daniel, et al.
(författare)
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Co-design Model for Neuromorphic Technology Development in Rolling Element Bearing Condition Monitoring
- 2023
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Ingår i: Proceedings of the Annual Conference of the PHM Society 2023. - : PHM Society.
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Konferensbidrag (refereegranskat)abstract
- This paper presents an end-to-end condition monitoring co-design model, from vibration measurement to anomaly detection, where conventional signal processing principles are combined with neuromorphic sensing and computing concepts to enable investigations of the potential improvements offered by brain-like information processing technologies.The use of machine learning in condition monitoring became increasingly popular for intelligent fault diagnosis in the last decade, taking advantage of the rapid developments in deep learning.However, the high computational cost of training and using deep neural networks prevents the use of such solutions for analysing the bulk of data generated by the resource constrained edge devices, i.e., the condition monitoring sensor systems, as only a minor fraction of data can be transmitted to the cloud or edge servers for analysis.There is an untapped potential to process this data and thereby improve intelligent fault diagnosis models using event-triggered sensing, spiking neural networks, and neuromorphic processors that substantially can improve the energy efficiency and capacity of embedded machine learning condition monitoring solutions.The proposed co-design model is evaluated on two use-cases involving rolling element bearing failures, one based on a labelled laboratory environment dataset, and one based on a wind turbine drivetrain bearing failure representing a real-world scenario with stochastic changes of machine state and unknown labels of the bearing condition.By adjusting co-design parameters, the resulting hybrid conventional/neuromorphic model show a comparable accuracy in detection performance for the laboratory dataset compared to the state-of-the-art reported in the literature.Similarly, for the wind turbine drivetrain dataset a bearing fault detection time comparable to that in previous work is obtained.This shows the successful implementation of a hybrid conventional/neuromorphic co-design model for condition monitoring applications, offering novel opportunities to investigate performance trade-offs and efficiency improvements enabled by neuromorphic technologies.
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