| 1. |
- Meyer, E., et al.
(författare)
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The state of the art in beyond 5G distributed massive multiple-input multiple-output communication system solutions
- 2022
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Ingår i: Open Research Europe. - : F1000 Research Ltd. - 2732-5121. ; 2
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Forskningsöversikt (refereegranskat)abstract
- Beyond fifth generation (5G) communication systems aim towards data rates in the tera bits per second range, with improved and flexible coverage options, introducing many new technological challenges in the fields of network architecture, signal pro- cessing, and radio frequency front-ends. One option is to move towards cell-free, or distributed massive Multiple-Input Multiple-Output (MIMO) network architectures and highly integrated front-end solutions. This paper presents an outlook on be- yond 5G distributed massive MIMO communication systems, the signal processing, characterisation and simulation challenges, and an overview of the state of the art in millimetre wave antennas and electronics.
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| 2. |
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| 3. |
- Waldschläger, Kryss, et al.
(författare)
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Microplastics: What Can We Learn from Clastic Sediments?
- 2023
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Ingår i: Springer Water. - : Springer Nature. - 2364-6934 .- 2364-8198. ; , s. 105-116
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Microplastics research has gained momentum in the 21st century but lags behind the long-standing research on clastic sediment.An interdisciplinary review paper was conducted, comparing microplastics with natural sediments in terms of particle properties, transport processes, sampling techniques, and ecotoxicology. The paper identifies seven research goals to enhance our understanding of microplastics in freshwater environments while learning from sediment research. This extended abstract presents the core message of the review paper, emphasizing the need to improve descriptions of microplastic particles, understand their transport processes, develop standardized sampling methods, and study their ecotoxicological effects. The research goals outline specific tasks to achieve these objectives and emphasize the importance of comparing microplastics to sediments to gain insights into their toxicity. Addressing these research goals will contribute to a comprehensive understanding of microplastics and their impact on freshwater ecosystems. For detailed insights, the original paper should be consulted.
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| 4. |
- Bilal, Muhammad Sajjad, et al.
(författare)
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Computational Investigation of 1, 3, 4 Oxadiazole Derivatives as Lead Inhibitors of VEGFR 2 in Comparison with EGFR : Density Functional Theory, Molecular Docking and Molecular Dynamics Simulation Studies
- 2022
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Ingår i: Biomolecules. - : MDPI. - 2218-273X. ; 12:11
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Tidskriftsartikel (refereegranskat)abstract
- Vascular endothelial growth factor (VEGF) is an angiogenic factor involved in tumor growth and metastasis. Gremlin has been proposed as a novel therapeutic pathway for the treatment of renal inflammatory diseases, acting via VEGFR 2 receptor. To date, most FDA-approved tyrosine kinase (TK) inhibitors have been reported as dual inhibitors of EGFR and VEGFR 2. The aim of the present study was to find the potent and selective inhibitor of VEGFR 2 specifically for the treatment of renal cancer. Fourteen previously identified anti-inflammatory compounds i.e., 1, 3, 4 oxadiazoles derivatives by our own group were selected for their anti-cancer potential, targeting the tyrosine kinase (TK) domain of VEGFR2 and EGFR. A detailed virtual screening-based study was designed viz density functional theory (DFT) study to find the compounds stability and reactivity, molecular docking for estimating binding affinity, SeeSAR analysis and molecular dynamic simulations to confirm protein ligand complex stability and ADMET properties to find the pharmacokinetic profile of all compounds. The DFT results suggested that among all the derivatives, the 7g, 7j, and 7l were chemically reactive and stable derivatives. The optimized structures obtained from the DFTs were further selected for molecular docking, and the results suggested that 7g, 7j and 7l derivatives as the best inhibitors of VEGFR 2 with binding energy values -46.32, -48.89 and -45.01 kJ/mol. The Estimated inhibition constant (IC50) of hit compound 7j (0.009 mu M) and simulation studies of its complexes confirms its high potency and best inhibitor of VEGFR2. All the derivatives were also docked with EGFR, where they showed weak binding energies and poor interactions, important compound 7g, 7j and 7i exhibited binding energy of -31.01, -33.23 and -34.19 kJ/mol respectively. Furthermore, the anticancer potential of the derivatives was confirmed by cell viability (MTT) assay using breast cancer and cervical cancer cell lines. At the end, the results of ADMET studies confirmed these derivatives as drug like candidates. Conclusively, the current study suggested substituted oxadiazoles as the potential anticancer compounds which exhibited more selectivity towards VEGFR2 in comparison to EGFR. Therefore, the identified lead molecules can be used for the synthesis of more potent derivatives of VEGFR2, along with extensive in vitro and in vivo experiments, that can be used to treat various cancers, especially renal cancers, and to prevent angiogenesis due to aberrant expression of VEGFR2.
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| 5. |
- Khan, Farooq-Ahmad, et al.
(författare)
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Designing Functionally Substituted Pyridine-Carbohydrazides for Potent Antibacterial and Devouring Antifungal Effect on Multidrug Resistant (MDR) Strains
- 2023
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Ingår i: Molecules. - : MDPI. - 1431-5157 .- 1420-3049. ; 28:1
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Tidskriftsartikel (refereegranskat)abstract
- The emergence of multidrug-resistant (MDR) pathogens and the gradual depletion of available antibiotics have exacerbated the need for novel antimicrobial agents with minimal toxicity. Herein, we report functionally substituted pyridine carbohydrazide with remarkable antimicrobial effect on multi-drug resistant strains. In the series, compound 6 had potent activity against four MDR strains of Candida spp., with minimum inhibitory concentration (MIC) values being in the range of 16-24 mu g/mL and percentage inhibition up to 92.57%, which was exceptional when compared to broad-spectrum antifungal drug fluconazole (MIC = 20 mu g/mL, 81.88% inhibition). Substitution of the octyl chain in 6 with a shorter butyl chain resulted in a significant anti-bacterial effect of 4 against Pseudomonas aeruginosa (ATCC 27853), the MIC value being 2-fold superior to the standard combination of ampicillin/cloxacillin. Time-kill kinetics assays were used to discern the efficacy and pharmacodynamics of the potent compounds. Further, hemolysis tests confirmed that both compounds had better safety profiles than the standard drugs. Besides, molecular docking simulations were used to further explore their mode of interaction with target proteins. Overall results suggest that these compounds have the potential to become promising antimicrobial drugs against MDR strains.
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| 6. |
- Salman, Muhammad, et al.
(författare)
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Evaluation of Conspiracy Beliefs, Vaccine Hesitancy, and Willingness to Pay towards COVID-19 Vaccines in Six Countries from Asian and African Regions : A Large Multinational Analysis
- 2022
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Ingår i: Vaccines. - : MDPI. - 2076-393X. ; 10:11
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Tidskriftsartikel (refereegranskat)abstract
- Vaccination protects people from serious illness and associated complications. Conspiracy theories and misinformation on vaccines have been rampant during the COVID-19 pandemic and are considered significant drivers of vaccine hesitancy. Since vaccine hesitancy can undermine efforts to immunize the population against COVID-19 and interferes with the vaccination rate, this study aimed to ascertain the COVID-19-vaccine-related conspiracy beliefs, vaccine hesitancy, views regarding vaccine mandates, and willingness to pay for vaccines among the general population. A web-based, cross-sectional survey was conducted (April-August 2021) among the adult population in six countries (Pakistan, Saudi Arabia, India, Malaysia, Sudan, and Egypt). Participants were recruited using an exponential, non-discriminate snowball sampling method. A validated self-completed electronic questionnaire was used for the data collection. All the participants responded to questions on various domains of the study instrument, including conspiracy beliefs, vaccine hesitancy, and willingness to pay. The responses were scored according to predefined criteria and stratified into various groups. All data were entered and analyzed using SPSS version 22. A total of 2481 responses were included in the study (Pakistan 24.1%, Saudi Arabia 19.5%, India 11.6%, Malaysia 8.1%, Sudan 19.3%, and Egypt 17.3%). There was a preponderance of participants <= 40 years old (18-25 years: 55.8%, 26-40 years: 28.5%) and females (57.1%). The average score of the COVID-19 vaccine conspiracy belief scale (C19V-CBS) was 2.30 +/- 2.12 (median 2; range 0-7). Our analysis showed that 30% of the respondents were found to achieve the ideal score of zero, indicating no conspiracy belief. The mean score of the COVID-19 vaccine hesitancy scale (C19V-HS) was 25.93 +/- 8.11 (range: 10-50). The majority (45.7%) had C19V-HA scores of 21-30 and nearly 28% achieved a score greater than 30, indicating a higher degree of hesitancy. There was a significant positive correlation between conspiracy beliefs and vaccine hesitancy (Spearman's rho = 0.547, p < 0.001). Half of the study population were against the vaccine mandate. Respondents in favor of governmental enforcement of COVID-19 vaccines had significantly (p < 0.001) lower scores on the C19V-CBS and C19V-HS scale. Nearly 52% reported that they would only take vaccine if it were free, and only 24% were willing to pay for COVID-19 vaccines. A high prevalence of conspiracy beliefs and vaccine hesitancy was observed in the targeted countries. Our findings highlight the dire need for aggressive measures to counter the conspiracy beliefs and factors underlying this vaccine hesitancy.
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| 7. |
- Tanveer, M Asjid, et al.
(författare)
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Deep learning-based auditory attention decoding in listeners with hearing impairment
- 2024
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Ingår i: Journal of Neural Engineering. - : IOP Publishing Ltd. - 1741-2560 .- 1741-2552. ; 21:3
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Tidskriftsartikel (refereegranskat)abstract
- This study develops a deep learning method for fast auditory attention decoding (AAD) using electroencephalography (EEG) from listeners with hearing impairment. It addresses three classification tasks: differentiating noise from speech-in-noise, classifying the direction of attended speech (left vs. right) and identifying the activation status of hearing aid noise reduction (NR) algorithms (OFF vs. ON). These tasks contribute to our understanding of how hearing technology influences auditory processing in the hearing-impaired population. Method: Deep convolutional neural network (DCNN) models were designed for each task. Two training strategies were employed to clarify the impact of data splitting on AAD tasks: inter-trial, where the testing set used classification windows from trials that the training set hadn't seen, and intra-trial, where the testing set used unseen classification windows from trials where other segments were seen during training. The models were evaluated on EEG data from 31 participants with hearing impairment, listening to competing talkers amidst background noise. Results: Using 1-second classification windows, DCNN models achieve accuracy (ACC) of 69.8\%, 73.3\% and 82.9\% and area-under-curve (AUC) of 77.2\%, 80.6\% and 92.1\% for the three tasks respectively on inter-trial strategy. In the intra-trial strategy, they achieved ACC of 87.9\%, 80.1\% and 97.5\%, along with AUC of 94.6\%, 89.1\%, and 99.8\%. Our DCNN models show good performance on short 1-second EEG samples, making them suitable for real-world applications. Conclusion: Our DCNN models successfully addressed three tasks with short 1-second EEG windows from participants with hearing impairment, showcasing their potential. While the inter-trial strategy demonstrated promise for assessing AAD, the intra-trial approach yielded inflated results, underscoring the important role of proper data splitting in EEG-based AAD tasks. Significance: Our findings showcase the promising potential of EEG-based tools for assessing auditory attention in clinical contexts and advancing hearing technology, while also promoting further exploration of alternative deep learning architectures and their potential constraints.
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