| 1. |
- Alharbi, Khalid Saad, et al.
(författare)
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Nuclear factor-kappa B and its role in inflammatory lung disease
- 2021
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Ingår i: Chemico-Biological Interactions. - : Elsevier. - 0009-2797 .- 1872-7786. ; 345
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Tidskriftsartikel (refereegranskat)abstract
- Nuclear factor-kappa B, involved in inflammation, host immune response, cell adhesion, growth signals, cell proliferation, cell differentiation, and apoptosis defense, is a dimeric transcription factor. Inflammation is a key component of many common respiratory disorders, including asthma, chronic obstructive pulmonary disease (COPD), bronchiectasis, and acute respiratory distress syndrome. Many basic transcription factors are found in NF-xB signaling, which is a member of the Rel protein family. Five members of this family c-REL, NF-xB2 (p100/ p52), RelA (p65), NF-xB1 (p105/p50), RelB, and RelA (p65) produce 5 transcriptionally active molecules. Proinflammatory cytokines, T lymphocyte, and B lymphocyte cell mitogens, lipopolysaccharides, bacteria, viral proteins, viruses, double-stranded RNA, oxidative stress, physical exertion, various chemotherapeutics are the stimulus responsible for NF-xB activation. NF-xB act as a principal component for several common respiratory illnesses, such as asthma, lung cancer, pulmonary fibrosis, COPD as well as infectious diseases like pneumonia, tuberculosis, COVID-19. Inflammatory lung disease, especially COVID-19, can make NF-xB a key target for drug production.
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| 2. |
- Borah, Jintu, et al.
(författare)
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AiCareBreath : IoT Enabled Location Invariant Novel Unified Model for Predicting Air Pollutants to Avoid Related Respiratory Disease
- 2024
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 11:8, s. 14625-14633
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Tidskriftsartikel (refereegranskat)abstract
- This article presents a location-invariant air pollution prediction model with good geographic generalizability. The model uses a Light GBR as part of a machine-learning framework to capture the spatial identification of air contaminants. Given the dynamic nature of air pollution, the model also uses a Random Forest to capture temporal dependencies in the data. Our model uses a transfer learning strategy to deal with location variability. The algorithm can learn concentration patterns because it has been trained on a vast dataset of air quality measurements from various locations. The trained model is then improved using information from a particular target site, customizing it to the features of the target area. Experiments are carried out on a comprehensive dataset containing air pollution measurements from various places to assess the efficacy of the proposed model. The recommended method performs better than standard models at forecasting air pollution levels, proving its dependability in various geographical settings. An interpretability analysis is also performed to learn about the variables affecting air pollution levels. We identify the geographical patterns associated with high pollutant concentrations by visualizing the learned representations within the model, giving important information for environmental planning and mitigation methods. The observations show that the model outperforms state-of-the-art forecasting based on RNNs and transformer-based models. The suggested methodology for forecasting air contaminants has the potential to improve air quality management and aid in decision-making across numerous regions. This helps safeguard the environment and public health by creating more precise and dependable air pollution forecast systems.
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| 3. |
- Pandya, Sharnil, Researcher, 1984-, et al.
(författare)
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COUNTERSAVIOR : AIoMT and IIoT enabled Adaptive Virus Outbreak Discovery Framework for Healthcare Informatics
- 2023
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662 .- 2372-2541. ; 10:4, s. 4202-4212
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Tidskriftsartikel (refereegranskat)abstract
- In the current Pandemic, global issues have caused health issues as well as economic downturns. At the beginning of every novel virus outbreak, lockdown is the best possible weapon to reduce the virus spread and save human life as the medical diagnosis followed by treatment and clinical approval takes significant time. The proposed COUNTERSAVIOR system aims at an Artificial Intelligence of Medical Things (AIoMT), and an edge line computing enabled and Big data analytics supported tracing and tracking approach that consumes GPS spatiotemporal data. COUNTERSAVIOR will be a better scientific tool to handle any virus outbreak. The proposed research discovers the prospect of applying an individual’s mobility to label mobility streams and forecast a virus such as COVID-19 pandemic transmission. The proposed system is the extension of the previously proposed COUNTERACT system. The proposed system can also identify the alternative saviour path concerning the confirmed subject’s cross-path using GPS data to avoid the possibility of infections. In the undertaken study, dynamic meta direct and indirect transmission, meta behaviour, and meta transmission saviour models are presented. In conducted experiments, the machine learning and deep learning methodologies have been used with the recorded historical location data for forecasting the behaviour patterns of confirmed and suspected individuals and a robust comparative analysis is also presented. The proposed system produces a report specifying people that have been exposed to the virus and notifying users about available pandemic saviour paths. In the end, we have represented 3D tracker movements of individuals, 3D contact analysis of COVID-19 and suspected individuals for 24 hours, forecasting and risk classification of COVID-19, suspected and safe individuals.
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| 4. |
- Ahmed, Tauheed, et al.
(författare)
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FIMBISAE : A Multimodal Biometric Secured Data Access Framework for Internet of Medical Things Ecosystem
- 2023
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 10:7, s. 6259-6270
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Tidskriftsartikel (refereegranskat)abstract
- Information from the Internet of Medical Things (IoMT) domain demands building safeguards against illegitimate access and identification. Existing user identification schemes suffer from challenges in detecting impersonation attacks which leave systems vulnerable and susceptible to misuse. Significant advancement has been achieved in the domain of biometrics and health informatics. This can take a step ahead with the usage of multimodal biometrics for the identification of healthcare system users. With this aim, the proposed work explores the fingerprint and iris modality to develop a multimodal biometric data identification and access control system for the healthcare ecosystem. In the proposed approach, minutiae-based fingerprint features and a combination of local and global iris features are considered for identification. Further, an index space based on the dimension of the feature vector is created, which gives a 1-D embedding of the high-dimensional feature set. Next, to minimize the impact of false rejection, the approach considers the possible deviation in each element of the feature vector and then stores the data in possible locations using the predefined threshold. Besides, to reduce the false acceptance rate, linking of the modalities has been done for every individual data. The modality linking thus helps in carrying out an efficient search of the queried data, thereby minimizing the false acceptance and rejection rate. Experiments on a chimeric iris and fingerprint bimodal database resulted in an average of 95% reduction in the search space at a hit rate of 98%. The results suggest that the proposed indexing scheme has the potential to substantially reduce the response time without compromising the accuracy of identification.
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| 5. |
- Bonagas, Nadilly, et al.
(författare)
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Pharmacological targeting of MTHFD2 suppresses acute myeloid leukemia by inducing thymidine depletion and replication stress
- 2022
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Ingår i: NATURE CANCER. - : Springer Science and Business Media LLC. - 2662-1347. ; 3:2, s. 156-
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Tidskriftsartikel (refereegranskat)abstract
- The folate metabolism enzyme MTHFD2 (methylenetetrahydrofolate dehydrogenase/cyclohydrolase) is consistently overexpressed in cancer but its roles are not fully characterized, and current candidate inhibitors have limited potency for clinical development. In the present study, we demonstrate a role for MTHFD2 in DNA replication and genomic stability in cancer cells, and perform a drug screen to identify potent and selective nanomolar MTHFD2 inhibitors; protein cocrystal structures demonstrated binding to the active site of MTHFD2 and target engagement. MTHFD2 inhibitors reduced replication fork speed and induced replication stress followed by S-phase arrest and apoptosis of acute myeloid leukemia cells in vitro and in vivo, with a therapeutic window spanning four orders of magnitude compared with nontumorigenic cells. Mechanistically, MTHFD2 inhibitors prevented thymidine production leading to misincorporation of uracil into DNA and replication stress. Overall, these results demonstrate a functional link between MTHFD2-dependent cancer metabolism and replication stress that can be exploited therapeutically with this new class of inhibitors. Helleday and colleagues describe a nanomolar MTHFD2 inhibitor that causes replication stress and DNA damage accumulation in cancer cells via thymidine depletion, demonstrating a potential therapeutic strategy in AML tumors in vivo.
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| 6. |
- Chaudhary, Rajat, et al.
(författare)
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A comprehensive survey on software-defined networking for smart communities
- 2022
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Ingår i: International Journal of Communication Systems. - : John Wiley & Sons. - 1074-5351 .- 1099-1131.
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Tidskriftsartikel (refereegranskat)abstract
- The need to provide services closer to the end-user proximity leads to the exchange of a large volume of data generated from the smart devices deployed at different geo-distributed sites. The massive amount of data generated from the smart devices need to be transmitted, analyzed, and processed. This requires seamless data exchanges among geo-separated nodes, which results in a considerable burden on the underlying network infrastructure and can degrade the performance of any implemented solution. Therefore, a dynamic, agile, and programmable network management paradigm is required. To handle the challenges mentioned above, software-defined networking (SDN) gained much attention from academia, researchers, and industrial sectors. Shifting the computational load from forwarding devices to a logically centralized controller is a dream of every network operator who wants to have complete control and global visibility of the network. Also, the concept of network functions virtualization (NFV) in SDN controller is required to increase resource utilization efficiency. Thus, in this paper, a comprehensive survey on SDN for various smart applications is presented. This survey covers the infrastructural details of SDN hardware and OpenFlow switches, controllers, simulation tools, programming languages, open issues, and challenges in SDN implementation with advanced technologies such as 5G and microservices. In addition, the challenges on the control plane and data plane are highlighted in detail, such as fault tolerance, routing, scheduling of flows, and energy consumption on OpenFlow switches. Finally, various open issues and challenges future scope of SDN are discussed and analyzed in the proposal.
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| 7. |
- Chaudhary, Rajat, et al.
(författare)
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SecGreen : Secrecy Ensured Power Optimization Scheme for Software-Defined Connected IoV
- 2023
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Ingår i: IEEE Transactions on Mobile Computing. - : IEEE Computer Society. - 1536-1233 .- 1558-0660. ; 22:4, s. 2370-2386
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Tidskriftsartikel (refereegranskat)abstract
- Software-Defined Internet of Vehicles (SD-IoV) is an emerging technology that is being used in modern intelligent transportation systems (ITS). The ultimate goal of SD-IoV is to provide seamless connectivity to the end-users with low latency and high-speed data transfer. However, due to the increase in the density of the connected IoV using an open channel, i.e., the Internet, the foremost challenges of high power consumption and secure data transfer are inevitable in such an environment. An external eavesdropper may intercept the transmitted message to access the legitimate information over the public channel, i.e., the Internet. Most of the solutions reported in the literature to tackle these issues may not be applicable in the SD-IoV environment due to high computation and communication costs. Motivated from this, in this paper, the problems of high power consumption and secure data transfer in SD-IoV are formulated using mixed-integer non-linear programming (MINLP) with associated constraints. To solve the aforementioned problem, we propose a joint power optimization and secrecy ensured scheme known as SecGreen. SecGreen has an efficient energy harvesting algorithm using simultaneous wireless information and power transfer (SWIPT) to maximize the energy efficiency. Moreover, to mitigate various security attacks, a resilient lightweight secrecy association protocol is designed between vehicle and trusted gateway node of SD-IoV so that only trusted vehicles can communicate with each other and with the nearest base stations. The secrecy association protocol uses security primitives such as- physically unclonable function (PUF), one-way hash function, and bitwise exclusive OR (XOR) operations which are suitable for energy-constraint sensors in SD-IoV. The performance of the SecGreen is compared with the existing schemes, Stable & Scalable Link Optimization (SSLO), and Secure & Energy-Efficient Blockchain-enabled (SEEB) respectively. The result shows that when the number of packets across the subchannel increases, the energy consumption increases. Also, the result shows that the proposed scheme attains 22.5% and 20.34% better energy efficiency as compared to SSLO and SEEB schemes, respectively. In addition, the SecGreen scheme achieves 37.48% and 32.15% higher throughput as compared to SSLO and SEEB schemes. The results obtained show the superior performance of the proposed SecGreen scheme in comparison to these existing competitive schemes in the literature.
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| 8. |
- Ghayvat, Hemant, et al.
(författare)
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Healthcare-CT : SoLiD PoD and Blockchain-Enabled Cyber Twin Approach for Healthcare 5.0 Ecosystems
- 2024
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 11:4, s. 6119-6130
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Tidskriftsartikel (refereegranskat)abstract
- The healthcare personals often use stored healthcare data to make crucial decisions, assess risk, and care for patients. The extraction of the required information from the saved healthcare data needs a healthcare ecosystem that can guarantee reliable data delivery. The reliability of cyber-physical data needs to be cross-examined using several sources of data of overlapping nature. The cross-examined data can be saved on blockchain and SOLID POD (SP) to preserve its reliability and privacy. Once the reliable healthcare data is stored on the blockchain and SP, the patients’ medical history can be delivered to data-operated systems to monitor, diagnose, and detect augmented healthcare anomalies. Cyber twins (CT) combine the specific cyber-physical objects with digital tools portraying their actual settings. The creation of a live model for the delivery of healthcare services presents a novel opportunity in patient care comprising better evaluation of risk and assessment without hampering the activities of daily living. The introduction of blockchain technology can improve the notion of CTs by certifying transparency, decentralized data storage, data irreversibility, and person-to-person industrial communication. The storage and exchange of CT data in the healthcare ecosystem depend on disseminated ledgers and decentralized databases for storing and processing data to avoid single point reliance. The present study develops an owner-centric decentralized sharing technique to fulfil the decentralized distribution of CT data.
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| 9. |
- Gupta, Rajeev Kumar, et al.
(författare)
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Biochar influences nitrogen and phosphorus dynamics in two texturally different soils
- 2024
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- Nitrogen (N) and phosphorus (P) are vital for crop growth. However, most agricultural systems have limited inherent ability to supply N and P to crops. Biochars (BCs) are strongly advocated in agrosystems and are known to improve the availability of N and P in crops through different chemical transformations. Herein, a soil-biochar incubation experiment was carried out to investigate the transformations of N and P in two different textured soils, namely clay loam and loamy sand, on mixing with rice straw biochar (RSB) and acacia wood biochar (ACB) at each level (0, 0.5, and 1.0% w/w). Ammonium N (NH4-N) decreased continuously with the increasing incubation period. The ammonium N content disappeared rapidly in both the soils incubated with biochars compared to the unamended soil. RSB increased the nitrate N (NO3–N) content significantly compared to ACB for the entire study period in both texturally divergent soils. The nitrate N content increased with the enhanced biochar addition rate in clay loam soil until 15 days after incubation; however, it was reduced for the biochar addition rate of 1% compared to 0.5% at 30 and 60 days after incubation in loamy sand soil. With ACB, the net increase in nitrate N content with the biochar addition rate of 1% remained higher than the 0.5% rate for 60 days in clay loam and 30 days in loamy sand soil. The phosphorus content remained consistently higher in both the soils amended with two types of biochars till the completion of the experiment.
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| 10. |
- Haslett, Sophie, 1988-, et al.
(författare)
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Nighttime NO emissions strongly suppress chlorine and nitrate radical formation during the winter in Delhi
- 2023
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Ingår i: Atmospheric Chemistry And Physics. - 1680-7316 .- 1680-7324. ; 23:16, s. 9023-9036
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Tidskriftsartikel (refereegranskat)abstract
- Atmospheric pollution in urban regions is highly influenced by oxidants due to their important role in the formation of secondary organic aerosol (SOA) and smog. These include the nitrate radical (NO3), which is typically considered a nighttime oxidant, and the chlorine radical (Cl), an extremely potent oxidant that can be released in the morning in chloride-rich environments as a result of nocturnal build-up of nitryl chloride (ClNO2). Chloride makes up a higher percentage of particulate matter in Delhi than has been observed anywhere else in the world, which results in Cl having an unusually strong influence in this city. Here, we present observations and model results revealing that atmospheric chemistry in Delhi exhibits an unusual diel cycle that is controlled by high concentrations of NO during the night. As a result of this, the formation of both NO3 and dinitrogen pentoxide (N2O5), a precursor of ClNO2 and thus Cl, are suppressed at night and increase to unusually high levels during the day. Our results indicate that a substantial reduction in nighttime NO has the potential to increase both nocturnal oxidation via NO(3 )and the production of Cl during the day.
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